AI RESEARCH
Rollout Pass-Rate Control: Steering Binary-Reward RL Toward Its Most Informative Regime
arXiv CS.LG
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ArXi:2605.05112v1 Announce Type: new SWE-bench-style agentic reinforcement learning relies on expensive stateful trajectories, yet substantial compute is wasted on sampled rollout groups with skewed pass rates, where binary rewards provide a weak contrastive signal. We frame this inefficiency as a pass-rate control problem and show that a 50% pass rate is the most informative operating point: it maximizes reward entropy, the probability of surviving group filtering, RLOO advantage energy under GRPO, and success--failure contrastive structure.