Beyond Entropy: Learning from Token-Level Distributional Deviations for LLM Reasoning
📰 ArXiv cs.AI
Learn to improve LLM reasoning by addressing entropy instability using token-level distributional deviations
Action Steps
- Identify token-level distributional deviations in LLM outputs using statistical methods
- Apply Reinforcement Learning with Verifiable Rewards (RLVR) to optimize LLM reasoning
- Monitor and adjust entropy levels to prevent collapse or explosion
- Evaluate LLM performance using metrics such as perplexity and accuracy
- Fine-tune LLMs using token-level distributional deviations to improve reasoning capabilities
Who Needs to Know This
NLP researchers and engineers working on LLMs can benefit from this approach to improve model reasoning and avoid premature convergence
Key Insight
💡 Token-level distributional deviations can help mitigate entropy instability in LLMs, leading to improved reasoning capabilities
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🤖 Improve LLM reasoning by addressing entropy instability with token-level distributional deviations! #LLMs #NLP #RLVR
Key Takeaways
Learn to improve LLM reasoning by addressing entropy instability using token-level distributional deviations
Full Article
Title: Beyond Entropy: Learning from Token-Level Distributional Deviations for LLM Reasoning
Abstract:
arXiv:2606.19771v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has significantly advanced Large Language Model (LLM) reasoning; however, it faces a fundamental optimization instability: uniform token updates precipitate entropy collapse, leading to premature convergence to suboptimal strategies, whereas excessive Shannon Entropy maximization can cause entropy explosion, driving blind exploration toward incoherent reasoning chains. To resolve this dichotomy,
Abstract:
arXiv:2606.19771v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has significantly advanced Large Language Model (LLM) reasoning; however, it faces a fundamental optimization instability: uniform token updates precipitate entropy collapse, leading to premature convergence to suboptimal strategies, whereas excessive Shannon Entropy maximization can cause entropy explosion, driving blind exploration toward incoherent reasoning chains. To resolve this dichotomy,
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