arXiv:2606.08543v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) improves large language model reasoning but often suffers from rapid policy-entropy collapse, where the policy prematurely concentrates on narrow high-probability reasoning paths. While global entropy regularization can encourage exploration, uniformly increasing entropy across all token positions is inefficient for long reasoning trajectories, where many tokens are not decision-relevant. We propose Position-Aware Entropy Calibration (PAEC), a token-level entropy-management framework that cons
The proliferation of large language models (LLMs) in reasoning tasks for reinforcement learning (RL) necessitates advanced calibration techniques to overcome current limitations like policy-entropy collapse.
Improving LLM reasoning in RL environments is crucial for developing more robust and efficient AI agents capable of complex decision-making and task execution.
The proposed PAEC framework offers a more efficient method for managing entropy within LLMs, potentially leading to faster and more stable development of intelligent AI systems.
- · AI researchers
- · LLM developers
- · Robotics sector
- · Generative AI platforms
- · Inefficient LLM reasoning methods
More effective and stable large language models for complex control and reasoning tasks.
Accelerated development and deployment of sophisticated AI agents across various industries.
Increased automation of white-collar workflows as agentic systems become more reliable and performant.
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Read at arXiv cs.AI