ESPO: Early-Stopping Proximal Policy Optimization

📰 ArXiv cs.AI

Learn how ESPO optimizes proximal policy optimization with early-stopping for more efficient reinforcement learning

advanced Published 29 May 2026

Action Steps

Implement ESPO by modifying the proximal policy optimization algorithm to include early-stopping criteria
Detect trajectory failure on-the-fly using a reward threshold or other heuristic
Terminate rollouts early when failure is detected to reduce compute waste
Compare the performance of ESPO with standard proximal policy optimization algorithms
Apply ESPO to large language models under reinforcement learning to improve training efficiency

Who Needs to Know This

Researchers and engineers working on reinforcement learning and large language models can benefit from this technique to improve training efficiency

Key Insight

💡 Early-stopping can significantly improve the efficiency of reinforcement learning by avoiding unnecessary computations

Key Takeaways

Learn how ESPO optimizes proximal policy optimization with early-stopping for more efficient reinforcement learning

Full Article

Title: ESPO: Early-Stopping Proximal Policy Optimization

Abstract:
arXiv:2605.29860v1 Announce Type: cross Abstract: When a large language model under reinforcement learning commits a wrong reasoning step early in a trajectory, standard algorithms force it to keep generating until the maximum horizon, spending compute on tokens that never receive positive reward and polluting advantage estimates with post-failure noise. We propose ESPO (Early-Stopping Proximal Policy Optimization), which detects trajectory failure on-the-fly and terminates rollouts early. At ea

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