mlsanityData to 14 Oct 2026

Probe the Harness: Setup Checks for Stale-Data RL Comparisons in Language Models

arXiv 2610.02911 ↗First posted 2 Oct 2026cs.LG cs.CLcited by 0

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Abstract (arXiv)

Methods for training language models on stale samples are judged by comparisons against importance-corrected baselines. We show that details of the experimental harness can reverse the observed ranking of methods, and we introduce PTH (Probe The Harness), a set of checks that makes the harness visible. Our case is a comparison between SAN, a behaviour-free method, and truncated importance sampling (TIS) on verl and in a single-GPU trainer, in which SAN first finished ahead in both stacks. Four details of the harness changed this comparison: the PPO ratio was taken against the learner's own recomputed probabilities, the data seed did not reach the TIS arm, the replay queue reused its first batch for 33 updates, and two loss normalisers differed from their description. In each case the logged quantity looked consistent with a working setup, while the quantity that defines the comparison went unchecked. With the harness checked, TIS matches SAN on verl, and in the trainer TIS learns steadily while SAN keeps a margin. We contribute the signature of each detail and its effect on the comparison, reference results for TIS and uncorrected GRPO under sampler lag, and the PTH checklist.

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References · 15

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  3. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,379
  4. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
  5. 2024HybridFlow: A Flexible and Efficient RLHF Frameworkcited by 2,057
  6. 2024Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvementcited by 1,090
  7. 2017Deep Reinforcement Learning that Matterscited by 754
  8. 2025AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoningcited by 250
  9. 2025A Sober Look at Progress in Language Model Reasoning: Pitfalls and Paths to Reproducibilitycited by 93
  10. 2025A-3PO: Accelerating Asynchronous LLM Training with Staleness-aware Proximal Policy Approximationcited by 12
  11. 2026Stable Asynchrony: Variance-Controlled Off-Policy RL for LLMscited by 11
  12. 2026How Off-Policy Can GRPO Be? Mu-GRPO for Efficient LLM Reinforcement Learningcited by 7
3 more
  1. 2026ASymPO: Asymmetric-Scale Policy Optimization for Asynchronous LLM Post-Training Without Behavior Informationcited by 3
  2. 2026Delightful Distributed Policy Gradientcited by 2
  3. 2026Staleness-Learning Rate Scaling Laws for Asynchronous RLHFcited by 2

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