The scripts are in the repository, not in the installed package. Clone it,
then
cd recipes/community before running the commands below. Browse this recipe on GitHub.airline-voice-concise-under-probe-outcome-filter
Behaviour: the agent runs long when the caller’s own text carries a planted instruction that buys length (“explain your reasoning step by step”, “take as long as you need”).
can-the-judge-be-trusted
Grade the same rollouts two ways — with rubric_judge() and with a deterministic verifier — and measure where the LLM judge disagrees with the rule.
can-the-markers-be-trusted
Check every behavioral marker against ground truth. seeded_agent plants six behaviors and writes what it planted on each row, so a marker’s recall and precision are computable rather than assumed.
force-the-branch
Seat: a post-training engineer at a startup that ships one production agent, trying to find out whether a small open model can take over the boring half of it.
grpo-group-size-at-fixed-budget
Sweep GRPO’s group size (num_generations) across G ∈ {2, 4, 8} while every arm spends the same 768 rollouts, and ask whether the knob moves held-out GSM8K accuracy past the noise floor.
hosted-grpo-vs-sft
Seat: an open-model fine-tuner who lives in TRL and Hugging Face, here for clean exports, a LoRA run, a hosted result and a cost line.
how-much-contamination-survives
Measure what fraction of real held-out contamination wai.decontaminate() actually removes, using human-labelled paraphrase pairs as ground truth instead of a planted copy.
identity-spec-no-unasked-maker-aas
Seat: a post-training engineer on a team that ships one production agent with a written identity and spec, trying to teach it who it is without it mentioning who it is for the rest of the day.
process-vs-outcome-reward
arXiv:2607.02869 (Palandye et al., July 2026) trains Qwen2.5-0.5B with GRPO on GSM8K under five reward regimes and reports that scoring the steps beats scoring the answer: 63.73% test accuracy for process-only against 5.
same-entrypoint-before-after
Seat: a post-training engineer at a startup that ships one production agent, trying to find out whether a small open model can take over the boring half of it.
the-step-the-course-skips
The eight-lesson course for people who have never trained a model stops one line short of the training: # 2.
what-trl-does-with-the-loss-mask
wai.export(…, format=“trl”) writes a loss_mask on every row and reports mask_mode: “assistant”.
which-half-can-a-small-model-take
I have one production agent and a cost line to defend.
who-protects-the-holdout
decontaminate() applies four rules in order.