arXiv:2604.11056v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) improves the reasoning ability of Large Language Models (LLMs), but sparse outcome rewards make token-level credit assignment difficult. We study token-level credit as a reward-conditioned shift from the behavior policy to a hindsight posterior. In autoregressive RLVR, this shift can be expressed through Conditional Mutual Information (CMI), which shows that token entropy upper-bounds possible hindsight credit. Entropy, however, indicates capacity rather than update direction, so we introd
This paper addresses a fundamental challenge in advanced AI development, specifically credit assignment in Reinforcement Learning with Verifiable Rewards (RLVR), which is crucial for building more robust and reasoning-capable LLMs.
Improving token-level credit assignment will lead to more efficient and effective training of LLMs, accelerating the development of agentic AI systems with better reasoning and decision-making capabilities.
The proposed 'signed-capacity view' offers a new theoretical framework for understanding and potentially improving how AI models learn from sparse rewards, moving beyond simple entropy measures for credit allocation.
- · AI Research Labs
- · LLM Developers
- · AI Agent Developers
- · AI Models with Limited Reasoning
- · Traditional RL Credit Assignment Methods
More efficient and reliable training of large language models for complex tasks.
Accelerated development of autonomous AI agents capable of sophisticated decision-making and problem-solving.
Enhanced AI capabilities across various sectors, potentially enabling new applications and automating more knowledge work.
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Read at arXiv cs.LG