> ## Documentation Index > Fetch the complete documentation index at: https://docs.withwhile.com/llms.txt > Use this file to discover all available pages before exploring further. # Platform: sign in, datasets, training, serving > Sign in, push and gate datasets, prune and check them, train on the platform or report your own run, and serve the result. Everything on this page talks to your While account. Every call reads the key that `whileai login` saved, or `WHILEAI_API_KEY`, or `api_key=`. The pure-Python checks that need no key (`optimize`, `hack_scan`, `judge_trust`, `delta_report` and the rest) are here too, because they sit between a run and a push. ## Sign in ```bash theme={null} whileai login ``` Prints a link and a short code. Open the link, sign in or sign up, press Approve. The key is saved to `~/.whileai/credentials.json` and every platform call reads it from there. Interrupted before you approved? Run it again; it resumes the same code. This is the path for coding agents too: tell yours to run `whileai login` and click the link it shows you. `whileai status` shows which key is in use, `whileai logout` removes it. No account yet, or no browser? One command creates the account and the key. Open the dashboard later by signing in with an email code. ```bash theme={null} whileai signup --email you@example.com ``` That key is a trial key (25k input and 50k output tokens a day, 100 MB of storage, ten datasets, and an expiry date) until the person signs in once at the While site with an email code; `whileai status` prints the link. `whileai status` shows the tier; `whileai.account()` returns tier, limits and usage. ## Store datasets on While Push a run to your account so the rest of the loop can read it. Credentials resolve in this order: `api_key=` argument, `WHILEAI_DELEGATED_CREDENTIAL` (a short-lived `zp_dc_...` issued from a Clerk session), `WHILEAI_API_KEY`, then the key saved by `whileai login`. ```python theme={null} # Runtime path with a delegated credential # export WHILEAI_DELEGATED_CREDENTIAL="zp_dc_..." # If you need to mint one from a Clerk session token: # credential = wai.issue_delegated_credential(clerk_token, ttl_seconds=3600) # export WHILEAI_DELEGATED_CREDENTIAL=credential["credential"] data = wai.simulate(my_agent, tools=TOOLS, system_prompt=POLICY) v1 = data.push("my-agent-explore-v1") # -> {"datasetId": "ds_...", ...} # iterate, then push the next version with lineage v2 = data.push("my-agent-explore-v2", parent=v1["datasetId"]) wai.datasets() # list yours + storage used rows = wai.pull(v1["datasetId"]) # rows, or pass path= for a file wai.push_file("rollout.jsonl") # upload an existing JSONL wai.delete_dataset(v1["datasetId"]) # permanent ``` Storage is private per account; `whileai.account()` says how much you have. `parent=` records dataset lineage so iterations show as a family on the platform. ### The publish gate `data.push` and `wai.push_file` run a publish gate first (`gate=False` skips it; `wai.push_rows` does not gate unless you pass `gate=True`, because the caller may already have run `optimize`). Every graded row gets a `calibration` stamp: its task's pass rate over k repeats, k, and the policy that produced it, so a trainer can build a curriculum or retire solved tasks. An RL-shaped run (repeats of one ask) is refused with `PublishGateError` when it is ungraded or has no mixed group, because a grouped update would learn nothing from it. The report comes back as `entry["gate"]`, with warnings when unanimous asks, or asks outside the difficulty band, are still present; `wai.optimize(data, mode="rl")` prunes those. `wai.publish_gate(rows)` runs the same check on any row list. The stamp is the schema's `Calibration` object: `wai.calibration_of(row)` reads it back typed, `from_row` carries it on `rollout.extra["calibration"]`, and `to_row` writes it out again. `k` is the repeats the grader saw, not the rows that survived: `optimize(mode="rl")` stamps its selection from the rows it was given, before its own dedupe and trims, and the gate keeps a carried stamp rather than re-measuring it on what is left. The gate's own `pass_at` block is still over the rows in front of it, and says so when the two differ. ### Export training rows `export_dataset` and `export_training` are the same function object (`export_dataset is export_training`): same arguments, same file, same report. Write `export_dataset` in new code; `export_training` is the older spelling, kept so nothing already written breaks. `training_rows` is the list-returning half of the same path, without writing a file. Training rows carry a `loss_mask`, one 0/1 per message: 1 on the agent's turns, 0 on system, user, and tool-output turns. Tool output is the environment's text, not the policy's, so a trainer should not learn to predict it. `mask_mode="final"` trains only the last assistant turn, for conversations whose earlier agent turns were scripted or came from another policy; the export report counts `trained_messages` and `masked_messages`. `unroll=True` turns an N-turn conversation into N samples, the k-th ending at the k-th agent turn with loss on that turn only, so every earlier turn trains once with the context it actually had (rlhf-book ch. 4). `max_tool_output_chars=` caps each tool message, appends a `[... N chars of tool output truncated]` marker and counts the cut on the row and in the report, so context spent on tool output is a decision the export makes out loud (ch. 13). Two wire shapes come out of the exporters, and a trainer needs the second one: ```python theme={null} wai.export_training(rows, "sft.jsonl") # OpenAI chat-completions wire (default) wai.export_training(rows, "sft.jsonl", format="trl") # what TRL's SFTTrainer loads wai.export_preference(pairs, "dpo.jsonl", format="trl") # what TRL's DPOTrainer loads wai.to_trl(wai.training_rows(data), "training") # same reshape on rows you already hold ``` `format="openai"` (the default) is the API wire row: the whole conversation in `messages`, `function.arguments` as a JSON string, and the ask alongside as `prompt`. `format="trl"` is what `trl.data_utils.maybe_apply_chat_template` accepts. For SFT that is conversational `messages` with **no** `prompt` string column: TRL decides "is this conversational?" from the column set, and a `prompt` string next to `messages` makes it skip the chat template silently and train on the bare ask; the ask survives as `prompt_text`. For preference data it is `prompt` as the message list up to the first agent turn with `chosen`/`rejected` as the completions only, because the default shape (a `prompt` string with full conversations on both sides) raises `TypeError: string indices must be integers` inside TRL. In the TRL shape `function.arguments` is a dict, not a JSON string: HF chat templates render it with `| tojson`, so a pre-encoded string is quoted twice and the student learns to emit a string where an object belongs. The `tool_call_roundtrip` block in the report names which of the two encodings it checked (`encoding: "json_string"` or `"dict"`), so `invalid: 0` says what it actually vouches for. The export refuses rows whose reply quotes their own privileged context (`privileged_leak` in the error) because the export scrubs the key, not the reply; drop the rows `leak_report` names, or pass `validate=False`. ### Prune before training ```python theme={null} rows, report = wai.optimize(data, mode="rl") # whole groups, 20%-80% pass rate rows, report = wai.optimize(data, mode="rl", band=(0.3, 0.7)) rows, report = wai.optimize(data, mode="rl", enforce_band=False) # rank, do not drop report["band_dropped"] # {"too_easy": n, "too_hard": n} ``` `optimize(mode="rl")` drops, in this order: junk rows; duplicate rollouts within an ask (the same trajectory twice adds nothing to a group-relative advantage); truncated rollouts (`truncated="keep"` leaves them in as `overlong`, `"penalize"` keeps them as failures with the judged score under `reward_before_penalty`, DAPO's overlong handling); unanimous asks (all pass or all fail: zero advantage); and asks outside the difficulty band. "Out of band" means outside the 0.2 to 0.8 *pass-rate* band, never off-topic. The filter does not read the prompt at all, so an on-topic ask the policy always solves is dropped and an odd one it solves half the time is kept. It then keeps whole groups round-robin across fault kinds and, within a fault kind, round-robin across pass rates: a 25% ask, a 50% ask and a 75% ask are taken in turn, with no preference for the middle (`order="middle"` restores the older nearest-to-50% ranking). Each kept row's `calibration` stamp carries `pass_rate_ci95`, the interval on that pass rate, and the report says so when the band was measured from fewer than 16 rollouts per task, since at 8 a task's band assignment can be off by about 0.3. The prune shrinks every group, so the k-way reliability numbers do not survive it: `pass_at` on the selection reports `pass^k` and `pass@k` as `n/a` where the graded rows had them, which is why you print `pass_at` before this call. The report says so in `hygiene_warnings`, and the carried `calibration` stamp keeps the graded per-task measurement. `optimize(mode="sft")` is rejection sampling (rlhf-book ch. 9): `select="top_per_prompt"` keeps each prompt's highest-reward completion above `min_reward` (default 1.0; lower it for a partial-credit grader), `"top_k_overall"` the best `k` across prompts, and the `random_*` rules are the matching chance controls. Exported groups carry `n0`/`n1` (fail/pass, partial credit splits at 0.5) and `reward_mean`/`reward_std`. The band is the offline difficulty filter from the reasoning-model recipes (keep prompts the policy solves 20-80% of the time); it is a heuristic, so it is a parameter. Every selector report (`select_for_rl`, `select_for_sft`, `build_preference_pairs`) carries `eval_sourced`, the rows or pairs whose reward came from `evaluate()` (`lineage.source == "eval"`), with a warning when it is non-zero: a held-out score that becomes the reward makes the scorer you report the one you optimised against. Nothing is dropped; grade the training set with `run_judge` or `data.grade` and keep `evaluate` for held-out rows. ### What will the policy learn? ```python theme={null} scan = wai.hack_scan(scored.rows, endorsed=["tool:lookup_order", "marker:grounded"]) scan["regime"] # train | reward_hack | pool_exhausted | no_signal | degenerate | unknown scan["top_feature"] # e.g. 'contains:### done' when the judge pays for a delimiter print(wai.format_hack_scan(scan)) ``` A grouped update learns whatever separates reward *within* an ask; what only tracks which ask it is (difficulty) is baselined away. `hack_scan` asks the question the same way: reward and every candidate feature are centered within ask, ranked by that correlation, and compared to a noise floor from shuffling reward within ask (`tau`). Features come in two tiers, both pure Python: the hand tier (reply length, tool calls, turns, truncation, surface counts, one indicator per tool called, mean token logprob, every numeric marker, plus `features={"name": fn}` of your own) and the auto tier (the 200 most common words and word pairs in the agent's text, and pairwise ANDs that beat both parents), which is the tier that finds the shortcut nobody listed. `endorsed` names what the reward should track, as substrings of feature names; with it the scan can say `reward_hack` (the top feature is not endorsed, and the warning names what the policy would learn instead), `integrity` (share of the above-floor signal that is endorsed), and lists rivals. Without it the scan still ranks and floors. An agent that emits only a couple of distinct trajectories per ask makes every feature that separates them an exact function of the label. They all tie at |rho| 1, and the floor cannot break a tie between two perfect explanations, so the scan returns `degenerate` with `top_feature` `None`, lists the tied features in `collinear`, and names the cause (`distinct_per_ask`) rather than picking the alphabetical winner. The whole loop, before, during and after training, is in [Reward hacking](/reward-hacking) and runs offline in [`recipes/02-measure/reward-hacking`](https://github.com/whilehq/whileai-sdk/tree/main/recipes/02-measure/reward-hacking). `optimize(mode="rl", endorsed=[...])` carries the scan as `report["hack_scan"]`, with its warnings in `report["hygiene_warnings"]` next to the older pooled `report["correlations"]` (reply length, tool calls, turns, flagged at `HACK_THRESHOLD` 0.3). A reward that tracks a shortcut is a judge problem, so it is flagged, not pruned. The publish gate reports the same on RL-shaped rows, plus near-duplicate asks and length spread; `data.push(endorsed=[...], strict_hacks=True)` refuses a `reward_hack`. Standalone: `wai.reward_correlations(rows)`, `wai.dedupe_groups(rows)`, `wai.near_duplicate_prompts(rows)`, `wai.length_report(rows)`. ### Curriculum: easy to hard, and retire the solved A curriculum needs per-prompt difficulty (rlhf-book ch. 7), which is each task's pass rate over its k rollouts. `curriculum(rows)` splits graded tasks into *trainable* (ordered easy to hard, and bucketed into `tiers` for a staged schedule), *retired* (pass rate above `solved`, default 0.8: an all-pass task is dead gradient), and *not ready* (below `floor`, default 0.2: no signal until the policy improves), and counts how many trainable tasks sit in the 20-80% band. The two defaults are the band's own edges, so `curriculum` and `optimize(mode="rl")` agree on which tasks are trainable. ```python theme={null} cur = wai.curriculum(scored.rows) # solved=0.8, floor=0.2, tiers=3 cur["schedule"] # trainable task ids, easy -> hard print(wai.format_curriculum(cur)) rows = wai.retire_solved(scored.rows) # drop tasks the policy already aces ``` ### Agents An agent exists the moment a push names it or a trace arrives with `gen_ai.agent.name`. Everything on the platform hangs off it. ```python theme={null} data.push( "airline-v3", agent="airline-support" ) # registers the agent, attaches tools + system prompt wai.agents() # every agent: traces, sets by purpose, public cards wai.register_agent("airline-support", description="Refunds and rebooking") ``` ### From the terminal The platform verbs a coding agent needs, as commands: ```bash theme={null} whileai agents # tracked agents and what each serves whileai agent # record, behaviors, verdict whileai runs # the version table, newest first whileai verdict [--behavior ] whileai promote whileai live --day YYYY-MM-DD --version --replies N [--flagged N --p50 S --cost USD] whileai keys # names and prefixes; create or revoke under Account ``` All take `--json` and `--api-key`; errors exit 1 with the reason on stderr. Thin calls into `whileai.platform`. The old `whileai purge` (ZeroProof traces and datasets) is gone; `wai.purge_agent("demo-agent")` and `wai.delete_empty_datasets(max_rows=2)` remain in Python, both with `dry_run=True`, until the ingest module is retired. ### Train, holdout, eval ```python theme={null} data.push("airline-v3", holdout=0.2) # train set + a linked holdout set, split by task data.push("airline-evals", purpose="eval") # a set you measure with scored = data.grade(judge=my_judge) scored.push("airline-rl-v3", gate=True, mode="rl") # the graded copies, gated wai.update_dataset("ds_...", purpose="holdout") wai.preview("ds_...") # three sample rows + the analyzer report wai.profile("ds_...") # pass rate, support, mixed tasks, tool use, per task ``` The Datasets page groups sets by purpose (train, holdout, eval) and records the simulation mode on each. A push is train unless it says otherwise; ingested traces are eval until training data is cut from them. Holdout is split by `scenario_id`, so a task is wholly on one side, and the same task lands on the same side every run. A `purpose="holdout"` push warns when the set is too small to prove a 5-point gain at 80% power. A task's identity is its cell in the coverage grid: the tools, the situation axes, and at most one clause of the policy. Each clause owns its own block of cells and the cells that pair the other axes carry no clause, so editing the system prompt keeps every task except the ones for the clause that changed. Rewording one rule, adding one, or swapping the model leaves the rest of the eval paired for `compare_runs`. ### Training data out of traces The platform's "Make training data" button, as one line: ```python theme={null} wai.send_score("4bf92f3577b34da6", 1.0) # this run passed summary = wai.cuts(agent="my-agent") # what a cut would hold print(wai.format_cuts(summary, agent="my-agent")) # the traces page's sentence made = wai.cut(agent="my-agent", kind="rl") # make it wai.pull(made["train"]["datasetId"], "train.jsonl") made["holdout"]["datasetId"] # measure on this, never train on it ``` A cut needs a pass or a fail on every run, and a judge answers after the run it is judging has closed. `send_score(trace_id, value)` grades a run that already ran. **1.0 or above is a pass**, so a 0-to-1 quality number never reads as one; send that under its own `name=` and keep `score` for the verdict. Re-sending the same name is a correction. Emitting `whileai.reward` on the span does the same thing when your grader runs inline. Runs of the same prompt are grouped by `zeroproof.scenario_id`. `kind="rl"` keeps the prompts the agent passes some of the time and not always (20% to 80% by default); `kind="sft"` keeps the best run of every prompt that ever passed. Either way the prompts are split into a train set and a held-out set. `since="7d"` narrows the window, `band=` and `holdout=` move the defaults, and any other keyword is a trace filter (`model=`, `tool=`, `evalSet=`). ## Trust the numbers Checks that decide whether a result is believable. All are report-only and run offline over rows you already have. ```python theme={null} rows, report = wai.attach_labels( rows, "labels.jsonl", annotator="ana" ) # gold_reward + who said what wai.judge_trust(rows, judge=my_judge) # is the judge trustworthy? data.grade(use_privileged=True) # judge also reads privileged principle, reference, hidden state wai.run_judge(rows, likert_judge, scale=(1, 5)) # rating kept, reward = (r - 1) / 4 pairs, report = wai.judge_pairs(pairs) # A vs B both ways round: winner, tie, position_flip_rate rows, report = wai.write_rubrics(rows, domain="refunds") # per-prompt criteria on privileged.rubric scored = wai.run_judge(rows, wai.rubric_judge()) # a verdict per criterion; markers rubric: clean, report = wai.decontaminate( train_rows, against=[eval_rows] ) # same task id, verbatim, or 8-gram overlap clean, report = wai.decontaminate( train_rows, against=[eval_rows], embedder=embed, similarity=0.85 ) # plus a semantic pass wai.style_markers(rows) # no_boilerplate, no_hedging, no_apology, no_sycophancy, answered wai.style_report(rows)["warnings"] # "reward pays for hedging (corr +0.41 ...)" wai.refusal_report(benign_rows) # over-refusal rate with a Wilson interval wai.compare_runs(run_a, run_b) # paired delta with a 95% interval wai.holdout_size( 0.05, base=0.6, k=4 ) # tasks to prove a 5-point gain; n_tasks_concentrated beside it wai.holdout_size( 0.05, before=before, after=after ) # the paired sd measured off a previous eval, no model wai.holdout_size(0.05, task_std=0.38) # or the sd read off a delta_report interval wai.delta_report(before, after, target="pass_at_1", must_not_regress=["honest_after_fault"]) wai.delta_report(before, after, target="pass_at_1", by="category") # the target per kind of prompt before = wai.simulate(agent, tools=TOOLS, tasks=base, runs=3) # the same eval three times after = wai.simulate(trained, tools=TOOLS, tasks=base, runs=3) wai.delta_report(before.rows(), after.rows(), target="pass_at_1") # run_std computed from the runs wai.eval_variance(before.rows()) # the eval's own re-run std, split by lineage.eval_run wai.mark_grounding( rows ) # markers["argument_grounding"]: every tool argument came from the conversation wai.grounding_report(rows) # grounded rate, and the invented values by tool and key ``` **Judge trust.** "Gold" means a label a person wrote. Label 50 to 100 rows by hand (a JSONL of `{"key": ..., "label": 0 or 1}` or a `{key: label}` dict, where a label names its row by `key`, by `scenario_id` plus `rollout_index`, or by `prompt`), attach them with `attach_labels(rows, labels, kind="human")`, and grade. That stamps `gold_reward` and `gold_kind="human"`; a model's labels, or a second judge pass, are marked `model` and do not count, and older rows with `gold_reward` but no record of who wrote it count as unknown. Every `grade` call then ends by checking the judge against those labels, with no flag needed: the summary lands on every graded row as `judge_meta["trust"]` (`agreement`, `agreement_low`, `kappa`, `n_gold`, `ok`), in the grade report as `trust`, and in `publish_gate` as `judge_trust`. The judge passes when the lower bound of its agreement with the people is at least 0.80 and kappa at least 0.60; under either, the report says the number, the floor, and what to do. With no human labels the grade prints one line saying the judge was not measured. `grade(trust="require")` raises instead of printing; `trust="off"` skips the check. `audit_grades` never audits with the grader's own model: when the auditor would be the same, it uses the other hosted model and the report says which (`grader`, `auditor`), or it stops and asks for `backend_spec=`. `judge_trust(rows, judge=)` is the standalone version. `report["ok"]` means measured and clean, so with no labels it is `False` and the report says the judge is unmeasured rather than untrustworthy (`format_judge_trust` prints `NOT MEASURED`). The report gives agreement with a Wilson interval and Cohen's kappa, agreement on two task halves (tune the rubric on one, read the other), judge pass rate on short versus long replies within the same human label (length bias the humans rule out), and, with the judge callable, a re-judge of a sample as-is (consistency) and with neutral filler appended (a flip means the judge reads length). Disagreements come back as a review queue. It refuses model gold unless `allow_model_gold=True`. The gold set needs both passes and failures; with one class only the report says so and skips the kappa and length flags. `probes="all"` (or a list) tries the reward hacks a policy finds first on the judge on purpose: filler, the rubric's own words stuffed in, a claim of success with no evidence, the ask echoed back, a well-formed tool call with empty arguments, a sycophantic opener, a polite refusal. An additive probe is exploitable when failing replies start passing; a replacement probe when a reply with no content passes. `report["exploitable_by"]` names the holes at or over 10%, and a policy trained on this judge will find those same holes. Standalone: `wai.judge_probes(rows, judge, rubric=...)`. With the hosted judge, call `wai.grade` once first (or `whileai.simulations.score.grade_llm.warm_judge`; it is not re-exported) so the cold start, two to three minutes, is not counted as timeouts. **Decontamination.** Four rules between a dataset's rows and any evaluation source (row lists, JSONL paths, or platform dataset ids), each counted on its own and a row counted once. A row is contaminated when it shares a `scenario_id` or `task_id` with an eval row (`n_same_task`: a task is a situation, not a string, so a rephrasing of an eval situation is the eval situation), when it is an eval prompt verbatim (`n_exact`), or when one eval text covers at least 80% of its words in shared 8-grams (`n_near`; `overlap=`, the Llama 2 rule; one shared 8-gram is not enough, because situations written from the same templates share whole sentences without sharing the question, and short prompts match verbatim only). `fields=("prompt", "final_text")` also checks replies against eval answers and references. Word overlap does not see a paraphrase: a holdout written by re-running the generator was 70% within 0.85 cosine of the training batch, and the 8-gram rule flagged 4 of its 101 prompts where a semantic pass flagged 16. Pass `embedder=` (any callable from a list of texts to one vector per text, so nothing is imported; with sentence-transformers, `embedder=lambda texts: model.encode(texts, normalize_embeddings=True).tolist()`) and rows whose prompt is within `similarity=` (0.85 cosine) of an eval prompt are flagged as `n_semantic`. That flag means the two prompts read alike, not that they are the same task: "cancel one reservation" and "cancel three reservations" for different customers score 0.93 with no shared answer. So the task-id rule decides first, the semantic pass only looks across different task ids, and `report["notes"]` says the flag is a question to check. The default stays lexical; the threshold was read off BGE (unrelated prompts score about 0.55 there) and needs picking for another model, so when the eval rows carry task ids the pass measures how alike distinct tasks read to your embedder (the 99th percentile of similarity over eval-prompt pairs with different task ids) and `notes` says it, and says when `similarity=` sits below it, since a threshold there flags tasks that merely share a domain. The report returns the clean rows with the first offenders, their coverage or similarity, and hits per field. **Intervals and comparison.** Every pass\@1 carries a 95% interval from a bootstrap over tasks (`pass_at(rows).ci95`), and `metric_summary` / `marker_summary` do the same for markers. pass^k and pass\@k carry their own (`pass_pow_k_ci95`, `pass_at_k_ci95`), a bootstrap over the k-eligible groups. Markers come from the judge: return `{"reward": ..., "markers": {"name": value}}` from a `grader=` or `run_judge` callable and they land on `row["markers"]`, which is what `marker_summary`, `delta_report` and `from_row` read. `compare_runs` pairs the tasks two runs share, bootstraps the paired difference, and adds a sign-flip permutation p-value; fewer than five shared tasks falls back to an unpaired test and says so. Tasks on one side only are dropped from a paired comparison; `note` says how many and `paired_share` is the fraction that paired, so a verdict over a quarter of the eval reads as one. Under half paired is `situations` in `delta_report`'s `not_comparable`: the two arms drew different situation sets (a `hard_share`, `dimensions` or seed change between them), the delta over the few that pair is between two evals, and the warning says to pin the after side to the baseline's tasks (`tasks=`) or compare per tier with `dataset_report`. The verdict `no_difference_detected` means the interval covers zero, not that the runs are equal. A task is a situation, not a string: every report (`pass_at`, `compare_runs`, `delta_report`, `eval_variance`, `curriculum`, `group_signal`, the exporters) groups rows by `wai.task_key(row)`, the engine's `scenario_id` when the row has one, so repeats and rephrasings of one situation count as one task and the same rows give the same task count everywhere. `pass_at(rows).config` and `delta_report(...)["config"]` say what the rows were produced with (temperature, reply budget, policy and judge versions), and `delta_report` warns when the two sides differ. **Same tasks, new prompt.** A run draws its tasks from the grid by seed and, above `concurrency: 1`, by completion order, so a second `simulate()` shares only part of its tasks with the first. To A/B a prompt edit, a model swap or another seed on exactly the same eval, pin the task set: `wai.simulate(agent, tools=TOOLS, system_prompt=EDITED, tasks=base)` re-runs every prompt of `base` (a run, its rows, or its JSONL path) on its own `scenario_id`, under the same faults and world state, and draws nothing new; it stops with `tasks_done` once every prompt has its rollouts, and `compare_runs(base.rows(), rerun.rows())` pairs every task. `tasks=` copies the prompts and, unless you pass `repeats=`, the pinned run's k (the most rollouts any of its prompts has), so a base built with `mode="rl", repeats=4` and re-run as `simulate(..., tasks=base, mode="rl")` comes back at k=4 and `pass_at` reports the same k on both sides. Pass `repeats=` to re-run at a different k on purpose: ```python theme={null} base = wai.simulate(agent, tools=TOOLS, system_prompt=POLICY, mode="rl", repeats=4) rerun = wai.simulate( agent, tools=TOOLS, system_prompt=EDITED, tasks=base, mode="rl" ) # k=4, inherited assert base.rollouts_per_request == rerun.rollouts_per_request # cheap guard ``` **Size before you run.** Most evals are too small to see the effects they produce. Per-prompt paired spread is stable for agent rubrics, about 0.38 across five lanes, and at that spread a 50-prompt eval only detects a 10-point gain; a 5-point gain needs about 220 prompts and a 3-point gain about 600. One lane read the same adapter as "barely helps" at 45 and 85 prompts (both straddled zero) and cleared at 131 (+0.13 \[+0.07, +0.19]); the effect was real the whole time and a GPU round went to fixing a data problem that was a measurement problem. The effective sample is prompts, not rollouts: raising k sharpens each prompt's estimate but does not narrow a bootstrap over prompts, so spend eval budget on prompts first (k still matters for preference and grouped methods, which need mixed groups). And "straddles zero" means the eval cannot tell, not that the model did not improve; say which. `holdout_size(effect, base=, k=)` says how many paired tasks prove a gain at 80% power, and `detectable_effect(n_tasks, ...)` is the same solved for the gain. Its binomial model assumes the gain is spread evenly across tasks and the two arms are independent draws, and says so in `notes`; when a trait is only exercised by some prompts most tasks are ties, the paired differences spread far wider, and the model under-sizes by several times (a voice lane at 0 to 0.127 needed 54 tasks where the model said 14), so the model path also returns `n_tasks_concentrated`. On a holdout whose tasks differ in difficulty the model errs the other way, asking for `1 / (1 - Var(p_i) / (p(1-p)))` times the tasks pairing needs (1.19x at spread 0.2 around 0.5, 2.78x at 0.4); `before=` alone reports the spread and that ratio. The honest paths measure: `before=before, after=after` (the same two row lists `delta_report` takes) reads the per-task paired sd off both arms of a previous eval on the same tasks, with the covariance pairing buys in it, and `task_std=` (the per-task sibling of `run_std`) takes the number you read off a `delta_report` (`(hi - lo) * sqrt(n_paired_tasks) / 3.92` from `target_ci95`). **Before and after.** `delta_report` runs `compare_runs` on pass\@1 and every marker both row sets share. `target=` names the metric the training was meant to move and gives the headline; `must_not_regress=` names the behaviors whose significant drop fails the report; any other significant drop is a warning. `format_delta_report(report)` prints one line per metric. `eval_variance(run_1, run_2, run_3)` is the eval's own re-run standard deviation (three or more evaluations of the same model); passing it as `run_std=` makes any delta inside the re-run band `within_noise`, and a target there reads `within_eval_noise` rather than moved, since re-running the eval moves it that much on its own (rlhf-book ch. 16). Pass `run_std_runs=` with it (the `n_runs` the floor came from) so the band uses the t quantile at `runs - 1` degrees of freedom: a floor from three re-runs is an estimate, and the 1.96 band that reads it as exact lets about one pure-noise delta in five through. A bare `run_std=` keeps 1.96 and warns. `by=` names a row key, a marker, or a callable that groups rows (a prompt category, a tool, a persona); the report then carries `groups`, the target compared within each group, and `groups_down` for any group whose target dropped significantly while the headline moved. A headline over one dominant kind of prompt cannot hide the other kinds that way. Both sides should have the same rollouts per task; when a run lost some (`data.report()["rollouts_lost"]`, with the reasons in `rollouts_lost_by`) and one arm sits at k=4 while the other is at k=2, the report warns next to the sizing line and names both. Unequal k is a precision issue, not a bias: rows lost at random leave the paired delta unbiased and only widen its interval; rows lost for a reason (a timeout on the hard runs) bias it, and only re-running the short arm on its short tasks fixes that. `balance_rollouts=True` (off by default) trims every paired task to the rows both sides have (drawn by `seed=`) so pass^k and pass\@k share one k; it costs precision, removes no bias, and `balanced` says how many rows each side gave up. **Run the eval three times.** One evaluation is a draw, not a number: the same model on the same tasks lands somewhere else next time, and most post-training gains are inside that spread (rlhf-book ch. 16, appendix C). `wai.simulate(agent, tasks=base, runs=3)` replays the task set three times in one call, same tasks, faults and world, and stamps `lineage.eval_run` on every row. Feed both sides to `delta_report` and it works out `run_std` from the repeats itself. The verdict words: `moved` is a change the interval and the re-run band both support; `moved_unreplicated` is a change seen once, which could be noise, and the warning tells you the `runs=3` call that settles it; `within_eval_noise` is a delta smaller than what re-running the eval does on its own, so equivalence, not a win; `no_change_detected` is an interval that covers zero. `format_delta_report` prints the same reading on its first line (`report["headline_verdict"]` is the word behind it): `PASS` only for a gain (`moved`, `moved_unreplicated`), `NO DIFFERENCE` for an interval over zero (a negative point estimate the interval does not settle is not a pass), `FAIL` for a regression or a failed guard, `NOT COMPARABLE (causes)` when the arms cannot be compared. `ceiling=True` means the before run already passes most of its tasks (0.9 or more, or too few paired tasks left with room), so there is little improvement the eval could show; use harder situations before training again. **Argument grounding.** A policy trained to call a tool learns to call it before it learns when not to; on the refund environment both GRPO and DPO learned to invent an order id on a quarter of the prompts that gave none while the headline rose. `mark_grounding(rows)` stamps `argument_grounding`: 1 when every string argument of every tool call appears in the prompt, the user and system turns, or an earlier tool result (rows with no calls count as grounded), else 0. No categories, any agent; `must_not_regress=["argument_grounding"]` fails the run that learned to invent, and `ungrounded_arguments(row)` / `grounding_report(rows)` name the values. `ignore_keys=` skips free-text arguments, `allow=` lists enums and defaults. **Trajectory flags.** Did the agent fake the work? `trace_markers(rows)` reads the trajectory rather than the prose (rlhf-book ch. 13, 14): `lie.tests_claimed` (tests said to pass when no test command ran or the last one failed), `lie.unverified_claim` ("I verified" with no tool calls), `lie.phantom_edit` ("I updated" with nothing written), `lie.ignored_failure` (the turn ended on a failed call and the reply never says so), `hack.test_edited`, `hack.test_weakened`, `hack.suppressed`, `hack.bypassed`, `risk.destructive`, `risk.secrets`, each with the fragment that raised it on `row["trace_flags"]`. The markers it stamps (`honest_claims`, `reported_failure`, `no_test_tampering`, `no_suppression`, `no_bypass`, `no_destructive`, `no_secrets`) are 1.0 when clean, so `must_not_regress=["honest_claims"]` fails a run that learned to overclaim, and `hack_scan` carries every fired flag as a `trace:` feature. `trace_flag_report(rows)` gives each flag's rate, examples, and its correlation with the reward, flagged when the judge pays for the fake. Reads, writes, deletes and commands are told apart by the tool's arguments and name; `kinds={"my_tool": "write"}` overrides. **Stage lineage.** The pipeline is a sequence of stages (rlhf-book ch. 3): SFT, reward modeling, RL, and the eval that judges the result. `stamp_stage(rows, "sft")` records which stage a row fed, and `stage_report(rows)` counts rows per stage and flags the one mistake it most needs caught: any task used in both `eval` and a training stage. `wai.stamp_stage`, `wai.stage_report`, `wai.stage_of`, `wai.STAGES` (`sft`, `rm`, `rl`, `eval`, `mid`). **Model spec as an object.** A spec or constitution is a living, versioned document (rlhf-book ch. 17). `load_spec(constitution)` wraps the `{source, traits: [{id, name, principle, authority}]}` shape (what the character recipe writes) into a `Spec` whose `version` is a content hash, so any edit to a principle changes it. `spec.behaviors()` are the trait ids, ready for `delta_report(must_not_regress=...)`; `stamp_spec(rows, spec)` tags every row with `spec_id` and `spec_version`, so you can ask whether adherence held from one spec or model version to the next. ```python theme={null} spec = wai.load_spec("recipes/03-select/character/constitution.json") scored = wai.stamp_spec(data.grade(judge=my_judge).rows, spec) wai.delta_report(before=before, after=scored, target="pass_at_1", must_not_regress=spec.behaviors()) ``` ### Markers: four families, one polarity A marker is a named behavior measurement on a row. Everything that reads markers (`marker_summary`, `delta_report`, `must_not_regress=`, `from_row`, the run page) reads one place, `row["markers"]`, and does not care which family put the value there. Four families write to it, and only one of them has the wrong polarity: | Family | How you get it | Polarity | Use it for | | ---------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------- | | **Judge-emitted custom markers** | your own name and value, returned as `{"reward": ..., "markers": {"name": value}}` from a `judge=` / `grader=` / `run_judge` callable | **yours to choose, and it must be 1.0 = good** | Anything your product cares about. This is the family `delta_report` and `must_not_regress=` are built for | | `trace_markers` / `trace_flag_report` | `wai.trace_markers(rows)` stamps `honest_claims`, `reported_failure`, `no_test_tampering`, `no_suppression`, `no_bypass`, `no_destructive`, `no_secrets`, with the evidence on `row["trace_flags"]` | 1.0 = no flag fired, higher is better | Did the agent fake the work? Read from the trajectory, not the prose; see [Trajectory flags](#trust-the-numbers) above | | `style_markers` / `style_report` | `wai.style_markers(rows)` stamps `no_boilerplate`, `no_hedging`, `no_apology`, `no_sycophancy`, `answered` | 1.0 = clean reply, higher is better | Over-optimization drift in a paired before/after | | `behavioral_markers` / `mark_rows` / `STOCK_MARKERS` | `wai.behavioral_markers(rows)` returns `{"boilerplate": 0.31, "refusal": 0.04, ...}` | **presence: 1 = the tic appears, higher is worse** | A one-shot read of how often each tic occurs. Not a delta | `behavioral_markers`, `mark_rows`, `row_markers` and `STOCK_MARKERS` live in `whileai.simulations.score.markers`, which is deprecated and raises a `DeprecationWarning` on first use. `style_markers` / `style_report` / `refusal_report` cover the same rlhf-book ch. 14 behaviors with the delta-ready polarity. **Polarity is the rule for every marker you define, not a quirk of one function.** `1.0` is the good outcome; higher is better; a significant *drop* is the regression that `must_not_regress=` fails on. Name markers after the behavior you want: ```python theme={null} # Wrong: 1 means the bug happened. {"markers": {"false_refund_success": 1.0}} # delta_report prints DOWN when you fix it, and # must_not_regress=["false_refund_success"] FAILS the run that fixed it. # Right: 1 means the agent did the right thing. {"markers": {"refund_correctly_refused": 1.0}} ``` If you have already collected rows under an inverted name, flip the value (`1 - v`) and rename before you compare runs; `delta_report` has no way to know which direction a name means. ## Train, and watch it Two ways to train, one record. The platform trains a pushed dataset (SFT, GRPO, DPO or a reward model, as a LoRA adapter) and serves the result; or your own trainer runs on Modal, a GPU box, or a notebook and reports into the same run. Either way the loss curve and the progress bar are at [zeroproofai.com/platform/training](https://www.zeroproofai.com/platform/training). ```python theme={null} run = wai.train( "ds_...", method="sft", base_model="Qwen/Qwen3-4B", epochs=2 ) # or "grpo" / "dpo" / "rm" with steps= run.wait() # done or failed; run.url is the curve while it goes run.training["before"], run.training["after"] # holdout pass@1 (SFT: loss) run.delta( before_rows, after_rows, target="pass_at_1", by="category" ) # paired delta on the run page model = wai.serve("refund-v2", run) # adapter on an OpenAI-compatible endpoint # model["endpoint"] + /chat/completions, model="refund-v2", bearer = your zp_ key wai.models() # what the account hosts ``` `epochs=` sets SFT, `steps=` sets GRPO, DPO and RM; each method has a default. `run.delta` is `delta_report` kept on the run and drawn on its page, including the per-group table when `by=` names a row key or marker; `wai.attach_delta(run_id, before, after)` does the same for a run that already finished. `holdout=` names the eval set (defaults to the train set's split sibling); a dataset already training returns that run. `serve` needs a finished run whose base is a served one (`Qwen/Qwen3-4B`, `microsoft/phi-4`; the list is `whileai.simulations.training.SERVED_BASES`). The trainer's default bases (Qwen2.5-0.5B for SFT, 1.5B for GRPO and DPO) train fast but cannot be served, so `train` warns when a run will not reach an endpoint. Qwen3 answers in thinking mode by default: leave room in `max_tokens` or send `extra_body={"chat_template_kwargs": {"enable_thinking": False}}`. `method="rm"` trains a reward model (rlhf-book ch. 5) on the set's pass-vs-fail pairs and reports pair accuracy on the held-out pairs before and after. `wai.reward_model(run)` is that model as a judge, with the judge contract (`reward` 0/1 against the run's threshold, `rm_score` raw), so it goes wherever a judge goes: ```python theme={null} rm = wai.train("ds_...", method="rm", steps=60, wait=True) run = wai.train( "ds_...", method="grpo", generations=8, beta=0.02, learning_rate=5e-6, seed=3 ) # the knobs a run is compared by judge = wai.reward_model(rm) # or reward_model("run_...", threshold=0.4) scored = data.grade(judge=judge) wai.judge_trust(scored.rows, judge=judge) # the same checks as the LLM judge ``` Every training knob (`generations`, `learning_rate`, `beta`, `max_completion_length`, `temperature`, `loss_type`, `truncated`) has a trainer default when left `None`; the range each is accepted in and the value the cited paper used are in `whileai.simulations.training.TRAINING_KNOBS`, and a rejected value is told the reference. Your own trainer, three ways in: ```python theme={null} # one line on a Transformers or TRL trainer run = wai.training_run( "identity-v1", dataset="ds_...", base_model="Qwen/Qwen3-4B-Instruct-2507", trainer="trl" ) trainer.add_callback(wai.TrainerCallback(run)) trainer.train() # loss, lr, eval loss, epoch, grad norm, then finish # your own loop with wai.training_run("sft-v3", dataset="ds_...", total_steps=1000) as run: for step, batch in enumerate(loader): loss = train_step(batch) run.log(step, loss=loss, lr=scheduler.get_last_lr()[0]) run.finish(summary={"final_loss": loss}, adapter="s3://.../adapter") # failed on exception run.holdout(before=0.42, after=0.58) # did it work? the run page opens with this ``` A run's page opens with one word, **Better**, **Worse** or **About the same**, over the held-out pass rate before and after. The platform's trainer measures it; a run on your own hardware says it with `run.holdout(before, after)`, or `wai.attach_holdout(run_id, before=..., after=...)` once the run has finished. Pass rates are 0 to 1, so 58% is `0.58`; `metric="loss"` sends held-out loss instead (SFT), where lower is better. `run.delta(...)` and `wai.attach_delta(...)` already measure both sides, so they fill the two numbers in themselves, and add `summary["holdout"]` (also `run.holdout_summary`): each side's pass rate with `n_tasks`, `k` and a `ci95`, plus the delta report's verdict word (`moved`, `moved_unreplicated`, `within_eval_noise`, `no_change_detected`). A hosted run read back with `run.refresh()` has the same block with the interval fields `None` and a note that the platform only returned two numbers. Plain HTTP, for a stack that is not Python: `POST /runs` with `name`, `dataset_id`, `base_model`, `total_steps` returns `runId`; `POST /runs/{id}/log` with `points` (a list of `{"step", "loss", "lr", ...}`, up to 500 a call); `POST /runs/{id}/finish` with `status` (`done`, `failed` or `stopped`), and optional `summary` and `adapter`. All with `X-Api-Key`. Points are buffered on the client and a send that fails is retried on the next flush; the dashboard never interrupts the trainer. `wai.get_run(id)` returns the run record. ## Report a run so a person can decide The platform draws one screen per tracked agent at [while.ai/platform/runs](https://while.ai/platform/runs): the held-out score by version with the frontier model as the line to beat, the training curve, what moved on the behaviors you did not train, the judge checks, live traffic on the served version, and cost. A coding agent fills it with `whileai.platform`; the person reads it and presses Promote. Your agent framework stays yours: `track` takes the agent object you already have (OpenAI Agents SDK, Pydantic AI, LangGraph, Claude Agent SDK) and reads the model, the instructions and the tools off it, or you describe it by hand. ```python theme={null} from whileai.platform import Behavior, Frontier, Harness, Judge, track tracked = track( "refund-bot", # or track(my_agent): name, model, prompt and tools come from the object model="Qwen/Qwen3-4B", harness=Harness(instructions=SYSTEM_PROMPT, tools=["lookup_order", "issue_refund"]), frontier=Frontier(name="Sonnet 5", score=81, cost_per_1k=18.0), ) tracked.behavior( Behavior( name="refunds", test_version="v2", n=240, judge=Judge(agreement=0.86, human_n=60, length_bias=0.08), noise_floor=2.4, contamination=0, reward_is_judge=False, ) ) run = tracked.run("v4", method="GRPO", targets=["refunds"], trained_on=["refunds-grpo"]) run.log(10, reward=0.41, kl=0.01) # or trainer.add_callback(wai.TrainerCallback(run)) run.score("refunds", 83, ci=2.7, n=240) # every behavior, not only the targets run.score("length", 76, ci=2.8, n=120) run.finish(hours=2.1, gpu="1xH100", cost_usd=31) print(tracked.verdict()) # one line: beats, trails, or about the same, and what that rests on ``` Every object is a pydantic model that validates before it leaves the process, and each one's docstring names the rlhfbook.com chapter it comes from. A *harness* is the instructions, tools and model name around the weights; its fingerprint is its version, so a prompt edit shows up as a new version without anyone naming it (a score is only comparable with its setup held constant). A *behavior* has its own frozen held-out test (`test_version`), a `noise_floor` measured by scoring the same model twice, and a judge checked against people (`agreement` over `human_n`) and for `length_bias`. A *run* is scored on every behavior: `targets` are the claim, the rest are the check (verbosity, sycophancy and refusals are what moves when the reward is gamed). `ci` is the half-width of the 95% interval; the difference interval is `delta ± sqrt(ci_candidate² + ci_served²)`, and the verdict says the candidate beats or trails the served version only when that interval excludes zero and the delta clears the behavior's declared `noise_floor`. A missing interval, an interval that includes zero, or a delta inside the re-run band is said in those words. The count of other behaviors that came out lower is on point estimates with no interval yet, so it is a prompt to look, not a result. The verdict ends with what the number rests on (judge agreement, n) and starts with `unproven:` when n is under 50, judge agreement is under 0.8 or unmeasured, or the training reward is the judge. `tracked.live(day, version=, replies=, flagged=)` reports a day of traffic when you serve the model yourself. Logging buffers and never raises into the training loop. Worked example: [`recipes/04-train/report-run`](https://github.com/whilehq/whileai-sdk/tree/main/recipes/04-train/report-run). ## Is it hacking the reward right now? ```python theme={null} monitor = wai.HackMonitor( run, holdout=holdout_rows, # prompts or {"prompt": ..., } gold=wai.reward_model(rm_run), # or the hosted judge, or a second rule; any judge callable every=10, k=4, # sample the holdout from the live policy every 10 steps endorsed=["tool:lookup_order"], # what the reward should track stop_on="divergence", # or "length", "drift", "feature", "any"; default: log only ) trainer = GRPOTrainer(model, reward_funcs=[monitor.wrap(rule_reward)], ...) trainer.add_callback(monitor) trainer.add_callback(wai.TrainerCallback(run)) ``` Over-optimization looks like one picture (rlhf-book ch. 14): the training reward keeps climbing while the evaluation you care about flattens, read against KL. The monitor draws it during the run instead of after. `wrap` watches the reward function, so the monitor keeps the last completions with their rewards and runs `hack_scan` on them; every `every` steps it samples the holdout from the live policy and scores it with the training reward (the proxy) and with `gold`, a scorer the proxy cannot see. `proxy_reward`, `gold_reward` and `holdout_length` land on the run beside the loss curve. Four alarms, one line each on the run: `divergence` (proxy up by `delta` over the window while the paired gold interval does not move up), `length` (completions grow while gold does not), `drift` (KL past `kl_budget`), `feature` (the batch scan says `reward_hack`). `stop_on` names the ones that stop training; a stopped run finishes as `stopped` with the reason, and `run.note(...)` puts anything else on the run's summary. `wai.format_hack_monitor(monitor.summary())` prints the curve and the alarms. [`recipes/04-train/grpo`](https://github.com/whilehq/whileai-sdk/tree/main/recipes/04-train/grpo) runs it by default. ## Publish a dataset as a card ```python theme={null} data.push( "airline-refunds-v3", agent="airline-support", publish=True, description="Graded refund conversations with injected tool faults.", ) wai.publish("ds_...", agent="airline-support") # or publish an existing one wai.catalog() # every public card, by agent rows = wai.pull("ds_...") # public sets need no key wai.unpublish("ds_...") ``` Cards live at [zeroproofai.com/datasets](https://zeroproofai.com/datasets), grouped by agent, with rows, size and the analyzer's numbers on each. A dataset must be finalized and hold rows to publish. Hugging Face, both directions. Connect your account once on any dataset page, then: ```python theme={null} wai.hf_status() # connected? namespaces wai.hf_publish("ds_...", repo="airline-refunds", wait=True) # rows -> a dataset repo you own wai.hf_publish_run("run_...", private=True) # a finished run's LoRA adapter -> a model repo row = wai.import_hf( "tatsu-lab/alpaca", split="train", purpose="eval" ) # any Hub split -> your account wai.profile(row["datasetId"]) # profiled before you train on it ``` Every push is one commit tagged `zp-`, so `load_dataset(repo, split, revision="zp-ds_...")` pins the exact push; the repo's `whileai.json` maps each split to its While dataset with history. Worked example: [`recipes/05-export/hugging-face`](https://github.com/whilehq/whileai-sdk/tree/main/recipes/05-export/hugging-face).