VEG: Verbal $\epsilon$-greedy for Semantic Exploration in Multi-Turn RL Agents

Yongchang Hao, Jie Hao, Yongsheng Mei, Ze Ye, Junyi Chai, Bin Guo, Benjamin Z. Yao, Chenlei Guo, Lili Mou

Keywords: LLM, RL, Agents, Multi-Turn

Abstract: Reinforcement learning (RL) has become a cornerstone of the post-training pipeline for large language models (LLMs), enabling capabilities such as complex reasoning and tool use. However, standard RL approaches face significant challenges due to reward sparsity. Moreover, LLMs typically exhibit mode-seeking behavior, concentrating probability mass on high-likelihood regions. This lack of diversity biases the model toward premature exploitation, hindering the exploration necessary for optimal learning. To address this, we propose VEG (verbal $\epsilon$-greedy), a novel framework that leverages external feedback as a dynamic control variable to explicitly balance exploration and exploitation within the semantic space. This method not only supplements sparse final rewards with intermediate signals but also enforces sustained exploration throughout the training process. Experiments on Tau Bench and SearchQA demonstrate that our method achieves superior accuracy compared to standard RL baselines. Notably, the trained policy eventually outperforms the external feedback model itself, demonstrating that VEG enables the agent to effectively filter and improve upon the guidance it receives.

Submission Type: Discovery

Submission Number: 281