[xeophon]

Keep successful empty environment outcomes in evaluation results. A trace-less episode or clean zero-turn trace retains the verdict returned by Verifiers, while training and curriculum logic exclude zero-turn traces.

Before

The dispatcher changed successful trace-less episodes into EmptyEpisode failures. Clean, non-timeout zero-turn traces became EmptyTrajectory failures, marking both the trace and episode unsuccessful. Evaluation therefore recorded a failure even when the environment had returned success, while training and curriculum filters relied on those injected errors to exclude empty traces.

flowchart TD
    Env["Verifiers returns an empty successful outcome"] --> Dispatch["Prime-RL dispatcher"]
    Dispatch --> Episode["No traces: inject EmptyEpisode"]
    Dispatch --> Trace["Clean zero-turn trace: inject EmptyTrajectory"]
    Episode --> Failed["Rewrite outcome as failed"]
    Trace --> Failed
    Failed --> Eval["Evaluation records a failure"]
    Failed --> Training["Training and curriculum filter injected errors"]

Loading

After

flowchart TD
    Env["Verifiers environment returns an episode"] --> Dispatch["Prime-RL dispatcher preserves empty outcome verdicts"]
    Dispatch --> Eval["Evaluation records and metrics"]
    Dispatch --> Filter["Training trace eligibility"]
    Filter --> Empty["Zero turns: no training sample"]
    Filter --> Sample["Eligible trace with sampled tokens: training sample"]
    Dispatch --> Curriculum["Curriculum ignores zero-turn traces and admits only trainable groups"]

Loading

Remove the synthetic EmptyEpisode and EmptyTrajectory errors. iter_trainable_traces() filters empty traces alongside failed and untrainable traces before advantages are assigned, so the advantage gate needs no separate zero-turn check. Difficulty sampling also excludes zero-turn traces. Empty groups finish without producing a trainer payload, and mixed groups retain their eligible traces. Curriculum admission also requires a trainable trace, so groups without training samples cannot inflate admission metrics. Sampler observations of completed turns are retained even when token metadata is unavailable. Verifiers' no-agent validation and training timeout handling remain in place.

#3824 applies the same ownership rule to partially failed episodes: preserve environment results, then decide training eligibility explicitly.

---

Note

Medium Risk
Changes evaluation outcome recording and which rollouts feed training/curriculum admission, which can shift eval metrics and training batch composition without touching auth or data stores.

Overview
Stops the dispatcher from injecting EmptyEpisode / EmptyTrajectory errors on successful trace-less or zero-turn outcomes, so evaluation keeps Verifiers’ success verdict instead of recording synthetic failures.

Training and curriculum now exclude zero-turn traces explicitly: iter_trainable_traces() skips num_turns == 0, difficulty-pool reward updates require num_turns > 0, and curriculum admission requires both passing gates and at least one trainable trace via iter_trainable_traces(). Scored rollouts without token/advantage metadata can still update sampler difficulty; groups with no trainable samples no longer count as admitted.

Tests cover mixed groups with non-trainable traces, empty-only groups, and reward-only observations for the sampler.

^{Reviewed by Cursor Bugbot for commit 8582fb8. Bugbot is set up for automated code reviews on this repo. Configure here.}

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[mikasenghaas]

ya this makes sense to me