Paper

Stale but Stable: Staleness-Adaptive Trust Regions for Stabilizing Asynchronous Reinforcement Learning

A staleness-aware PPO variant that contracts only the risky side of the clip interval for high-mismatch samples, leaving normal updates unchanged.

arXiv ↗Jul 21, 2026Code ↗

  • rl
  • policy optimization
  • asynchronous rl
  • ppo
Shows core claim: GSPO training collapse, key comparative finding of the work
Shows core claim: GSPO training collapse, key comparative finding of the work

What it is

Adapts PPO clipping to rollout staleness in asynchronous RL, tightening only high-mismatch updates to stabilize training under policy lag and engine mismatch. Instead of PPO's fixed clip for all tokens, SAT shrinks only the outward clip boundary on high-staleness samples, using detached log-ratio as a staleness proxy; ordinary and pull-back updates stay unchanged.

Key results

  • 35.83 AIME24 avg@8 at lag 1 with SAT-GSPO w/ R3
  • 34.79 AIME24 avg@8 at lag 8 with SAT-GSPO w/ R3
  • 34.17 AIME24 avg@8 at lag 1 with SAT-GSPO

Shows that matching trust-region strength to per-sample staleness can stabilize asynchronous RL without globally shrinking all updates.

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