whileai 0.75

MID-TRAINING AND POST-TRAINING FOR LANGUAGE MODELS

whileai makes training and eval data for agents that call tools. Give it an agent, or just the agent's tools and system prompt. It writes the situations the agent might meet, runs the agent through them against a fake world that fails on purpose, and hands back every conversation as a row. You grade the rows with your own judge or a verifier. The package then does the bookkeeping that is easy to skip and expensive to get wrong: pass rates with intervals, difficulty bands for RL, a check that your judge agrees with people, decontamination against your eval set, and a scan for rewards the policy can game. Every method says where it comes from (References).

uv add whileai

Or pip install whileai. Python 3.10 to 3.13, one dependency, typed. This package used to be called zeroproof; that name still installs it.

Two ways in

You only want evals. Plenty of teams cannot train and still need to know whether the last prompt edit helped. Run whileai init-evals in your project. It finds your agent, writes a judge and a runner around it, and gives you a pass rate with a 95% interval, a table of where the agent fails, and a test that goes red in CI when it gets worse. coverage_gap tells you which situations your tests never reach. compare_runs reruns the same tasks after a prompt or tool change and says whether the change helped. Start at docs/evals.md.

You want to train. Grade the same rows, keep the ones that carry signal, export. That is the rest of this page.

Sixty seconds, offline

No key, no network. seeded_agent is a stand-in agent. It answers honestly most of the time and, on a labeled fraction of rollouts, does one thing wrong on purpose: hedges, flatters, or claims success after a tool failed. Each row records what it did in seeded, so you can check that your judge catches exactly those rows before you trust it on real ones.

import whileai.simulations as wai

TOOLS = [
    {
        "type": "function",
        "function": {
            "name": "get_order",
            "description": "Look up an order by id.",
            "parameters": {
                "type": "object",
                "properties": {"order_id": {"type": "string"}},
                "required": ["order_id"],
            },
        },
    }
]

data = wai.simulate(
    wai.seeded_agent(TOOLS),
    tools=TOOLS,
    system_prompt="Help customers with orders.",
    simulator=False,  # no model
    mode="rl",
    repeats=4,
    repeat_policy="fixed",
    budget=64,
)
scored = data.grade(judge=lambda row: {"reward": int(not row["seeded"])})
print(scored.pass_at)

pass@1 0.67 [0.55..0.78] | pass^4 (pass_pow_k) 0.19 [0.00..0.38] | pass@4 1.00 [1.00..1.00] | headroom 0.33 (16 groups, k=4)

pass@1 is the pass rate over tasks with a bootstrap interval. pass^4 is how often all four rollouts of a task pass. Headroom is pass@4 minus pass@1, the gap an RL update could close.

To use your own agent, pass any callable that takes the user message and returns {"steps": [...], "final_text": "..."}. To use a model, pass a spec string: openai:<model>, anthropic:<model>, vllm:<model>@<url>, or ollama:<model>. With no agent= at all, the run uses the Qwen we host, on your key from whileai login, and Phi-4 grades. The judge is never the model it is judging.

The loop

The science

SFT. optimize(mode="sft") is rejection sampling [14], [16]: keep the best-scoring completion for each prompt, with a random selector alongside so you can tell whether picking the best did anything. Exported rows carry a loss_mask per message, so the trainer learns from the agent's turns and not from tool output. unroll=True splits a long conversation into one sample per agent turn, each with the context that turn actually saw. format="trl" is the shape SFTTrainer loads [1, ch. 4].

RL with verifiable rewards. When a program can check the answer, the reward should be that program [5]: MathEqual, CodeExec against hidden tests, JSONSchema, and combinations of them. In mode="rl" every prompt gets two rollouts first. Only prompts where those two disagree are filled to k, because a group that all passes or all fails has zero advantage under GRPO [19]. That is DAPO's dynamic sampling [12], applied while the rollouts are generated instead of after. optimize(mode="rl") then keeps the prompts the policy solves 20 to 80% of the time [13] and lets you choose what happens to rollouts that hit the length cap [12]. export_environment writes the tasks, the fake world and the reward as a verifiers package you can hand to a trainer. Rows keep their sampling logprobs so the trainer can form the importance ratio [21], and mean_kl measures drift from the reference model [22].

Character training. Write down how the model should talk as a constitution [23], [24]. load_spec hashes it into spec.version, so an edit to one principle is a new version. The judge is checked against the labels the spec itself carries before it grades anything. Preference pairs are matched on length [7], so the model learns the trait and not "longer is better". Put spec.behaviors() in must_not_regress and delta_report fails any run that improved one trait by giving up another. docs/character-training.md.

Evaluation. Intervals are bootstrapped over tasks, not rollouts, because rollouts of the same task are not independent [8], [10], [11]. Run an eval three times with runs=3 and delta_report refuses to call a change real when it sits inside twice the run-to-run standard deviation. holdout_size says how many prompts you need to see a given gain at 80% power [11]; most evals are too small. decontaminate checks training rows against the eval set with the 80% n-gram overlap rule [16], and with embeddings when you pass an embedder. docs/evals.md.

Over-optimization. The reward is a proxy for what you want, and RL finds the gap between the two [17]. hack_scan looks for the feature that predicts reward within a task, against a shuffled baseline, so a judge that pays for a phrase or a delimiter shows up before you train on it. judge_probes tries the tricks a policy finds first, flattery included [18]. delta_report(proxy=, target=) fails when the training reward went up and the metric you care about did not. HackMonitor runs the same scan inside a TRL training loop and can stop it. docs/reward-hacking.md.

Recipes

Each recipe is one script and a README that says what you learn, what you need, and how long it takes. All of them run in CI.

Platform

Push a graded run to your While account, train on it, serve the result. push refuses RL data with no mixed groups, since a trainer would learn nothing from it.

v1 = data.push("refunds-v1", holdout=0.2, gate=True)
run = wai.train(v1["datasetId"], method="grpo", steps=200)  # sft | grpo | dpo | rm
run.wait()
model = wai.serve("refunds-v2", run)  # OpenAI-compatible endpoint

If you train with your own code, wai.TrainerCallback reports into the same run page. Traces from production come back through traces=, which points the next simulation at the situations that failed.

Documentation

docs/reference.md: every call, knob, report and gate. docs/engine.md: how a row is made. CHANGELOG.md: one entry per release.

Development

uv sync --extra dev
uv run pytest
uv run ruff check . && uv run mypy

CI runs the suite on Python 3.10 to 3.13, gates coverage at 90%, and runs every recipe's smoke.sh. CONTRIBUTING.md.

Cite

@software{weiss2026whileai,
  title  = {whileai: post-training data and evaluation for tool-using agents},
  author = {Weiss, Jacob},
  year   = {2026},
  url    = {https://github.com/whilehq/whileai-sdk}
}

References

1. Lambert, N. Reinforcement Learning from Human Feedback. arXiv:2504.12501, 2025.
2. Kuhn, D. R., Wallace, D. R., Gallo, A. M. Software Fault Interactions and Implications for Software Testing. IEEE TSE 30(6), 2004.
3. Yao, S. et al. τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains. arXiv:2406.12045, 2024.
4. Ouyang, L. et al. Training Language Models to Follow Instructions with Human Feedback. NeurIPS, 2022.
5. Lambert, N. et al. Tülu 3: Pushing Frontiers in Open Language Model Post-Training. arXiv:2411.15124, 2024.
6. Cohen, J. A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement 20(1), 1960.
7. Zheng, L. et al. Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena. NeurIPS, 2023.
8. Chen, M. et al. Evaluating Large Language Models Trained on Code. arXiv:2107.03374, 2021.
9. Wilson, E. B. Probable Inference, the Law of Succession, and Statistical Inference. JASA 22(158), 1927.
10. Efron, B., Tibshirani, R. J. An Introduction to the Bootstrap. Chapman & Hall, 1993.
11. Miller, E. Adding Error Bars to Evals. arXiv:2411.00640, 2024.
12. Yu, Q. et al. DAPO: An Open-Source LLM Reinforcement Learning System at Scale. arXiv:2503.14476, 2025.
13. He, J. et al. Skywork Open Reasoner 1 Technical Report. arXiv:2505.22312, 2025.
14. Yuan, Z. et al. Scaling Relationship on Learning Mathematical Reasoning with Large Language Models. arXiv:2308.01825, 2023.
15. Rafailov, R. et al. Direct Preference Optimization. NeurIPS, 2023.
16. Touvron, H. et al. Llama 2: Open Foundation and Fine-Tuned Chat Models. arXiv:2307.09288, 2023.
17. Gao, L., Schulman, J., Hilton, J. Scaling Laws for Reward Model Overoptimization. ICML, 2023.
18. Sharma, M. et al. Towards Understanding Sycophancy in Language Models. ICLR, 2024.
19. Shao, Z. et al. DeepSeekMath. arXiv:2402.03300, 2024.
20. DeepSeek-AI. DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning. arXiv:2501.12948, 2025.
21. Schulman, J. et al. Proximal Policy Optimization Algorithms. arXiv:1707.06347, 2017.
22. Ziegler, D. M. et al. Fine-Tuning Language Models from Human Preferences. arXiv:1909.08593, 2019.
23. Bai, Y. et al. Constitutional AI: Harmlessness from AI Feedback. arXiv:2212.08073, 2022.
24. OpenAI. Model Spec, 2024. model-spec.openai.com.

License

Apache-2.0