Risk-Sensitive RL for Alleviating Exploration Dilemmas in Large Language Models

• URL: http://arxiv.org/abs/2509.24261v1
• Date: Mon, 29 Sep 2025 04:12:20 GMT
• Title: Risk-Sensitive RL for Alleviating Exploration Dilemmas in Large Language Models
• Authors: Yuhua Jiang, Jiawei Huang, Yufeng Yuan, Xin Mao, Yu Yue, Qianchuan Zhao, Lin Yan,
• Abstract summary: Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective for enhancing Large Language Models (LLMs)<n>We introduce a Risk-Sensitive Reinforcement Learning framework.<n>Our approach employs a risk-seeking objective that interpolates between mean and maximum rewards, leading to a novel algorithm.<n>Remarkably, RS-GRPO is simple to implement, requiring only minor code modifications.
• Score: 22.50153462109328
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective for enhancing Large Language Models (LLMs) on complex reasoning tasks. However, existing methods suffer from an exploration dilemma: the sharply peaked initial policies of pre-trained LLMs confine standard RL algorithms to a narrow set of solutions, boosting single-solution accuracy (pass@1) but suppressing solution diversity and multi-solution performance (pass@k). As a result, RLVR often distills existing capabilities rather than discovering new reasoning strategies. To overcome this, we introduce a Risk-Sensitive Reinforcement Learning framework. Our approach employs a risk-seeking objective that interpolates between mean and maximum rewards, leading to a novel algorithm, Risk-Sensitive GRPO (RS-GRPO), which drives deeper exploration by amplifying learning from challenging prompts. Remarkably, RS-GRPO is simple to implement, requiring only minor code modifications. On six mathematical reasoning benchmarks and with five different LLMs, RS-GRPO consistently improves pass@k performance while maintaining or enhancing pass@1 accuracy.

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