Targeted Exploration via Unified Entropy Control for Reinforcement Learning

2026-04-15 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

The Unified Entropy Control for Reinforcement Learning (UEC-RL) framework addresses entropy collapse and training instability in large language models (LLMs) and vision-language models (VLMs) trained with Group Relative Policy Optimization (GRPO). GRPO often suffers from premature policy convergence and loss of diversity due to rapid entropy decrease. UEC-RL introduces a targeted exploration mechanism that activates higher entropy reasoning on difficult prompts, expanding the search space for valuable reasoning trajectories. Concurrently, a controllable entropy stabilizer prevents uncontrolled entropy growth, reinforcing reliable behaviors and ensuring stable convergence. Experiments on LLM and VLM reasoning tasks, including Geometry3K, demonstrate UEC-RL's consistent gains over RL baselines in Pass@1 and Pass@k, achieving a 37.9% relative improvement over GRPO on Geometry3K. The code is available on GitHub.

Key takeaway

For research scientists developing or fine-tuning large language and vision-language models, UEC-RL offers a robust solution to common training challenges. You should consider integrating UEC-RL to mitigate entropy collapse and enhance training stability, especially for complex reasoning tasks. This approach can lead to significantly improved accuracy and more diverse, reliable policy distributions compared to traditional GRPO methods.

Key insights

UEC-RL balances exploration and stabilization through bidirectional entropy control to improve reasoning in large models.

Principles

• Entropy collapse limits policy diversity.
• Targeted exploration improves reasoning on difficult problems.
• Stabilization prevents uncontrolled entropy growth.

Method

UEC-RL identifies difficult prompts, expands sampling with a softened distribution (temperature t'), and selectively retains informative trajectories. A stabilizer then reinforces high-quality trajectories via replay to decrease entropy.

In practice

• Use UEC-RL for LLM/VLM reasoning tasks.
• Apply targeted exploration on challenging prompts.
• Employ entropy stabilization to ensure convergence.

Topics

• Policy Optimization
• Entropy Control
• Targeted Exploration
• Large Language Models
• Vision-Language Models

Code references

• 597358816/UEC-RL

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.