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New Relative Surprisal Index enhances LLM reasoning in RLVR

Researchers have introduced the Relative Surprisal Index (RSI), a new metric for Reinforcement Learning with Verifiable Rewards (RLVR) in large language models. RSI aims to reconcile conflicting approaches in RLVR by considering both token entropy and probability. The proposed RSI Selection (RSI-S) method filters tokens within a stable RSI interval, removing both redundant and unstable tokens. Empirical results show RSI-S improves accuracy on benchmarks like AIME and AMC across various Qwen2.5 model scales. AI

IMPACT Introduces a novel metric and filtering method that could improve LLM reasoning capabilities in RLVR applications.

RANK_REASON The cluster contains an academic paper detailing a new metric and method for improving LLM reasoning.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New Relative Surprisal Index enhances LLM reasoning in RLVR

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The cluster contains an academic paper detailing a new metric and method for improving LLM reasoning.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Outongyi Lv, Yanzhao Zheng, Yuanwei Zhang, Zhenghao Huang, Xingjun Wang, Baohua Dong, Hangcheng Zhu, Yingda Chen ·

    Which Tokens Matter? Adaptive Token Selection for RLVR with the Relative Surprisal Index

    arXiv:2606.31575v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a powerful tool for propelling Large Language Models (LLMs) beyond imitation-based training towards more robust reasoning capabilities. Among existing approaches, RL with Verifiable Rewards (RL…

  2. arXiv cs.AI TIER_1 English(EN) · Yingda Chen ·

    Which Tokens Matter? Adaptive Token Selection for RLVR with the Relative Surprisal Index

    Reinforcement learning (RL) has become a powerful tool for propelling Large Language Models (LLMs) beyond imitation-based training towards more robust reasoning capabilities. Among existing approaches, RL with Verifiable Rewards (RLVR) has emerged as a pivotal paradigm for advanc…