A Simple "Try Again" Can Elicit Multi-Turn LLM Reasoning
Authors: name: Licheng Liu, name: Zihan Wang, name: Linjie Li, name: Chenwei Xu, name: Yiping Lu, name: Han Liu, name: Avirup Sil, name: Manling Li

Multi-turn problem solving is critical yet challenging for Large Reasoning
Models (LRMs) to reflect on their reasoning and revise from feedback. Existing
Reinforcement Learning (RL) methods train large reasoning models on a
single-turn paradigm with verifiable rewards. However, we observe that models
trained with existing RL paradigms often lose their ability to solve problems
across multiple turns and struggle to revise answers based on contextual
feedback, leading to repetitive responses. We ask: can LRMs learn to reflect
their answers in a multi-turn context? In this work, we find that training
models with multi-turn RL using only unary feedback (e.g., "Let's try again")
after wrong answers can improve both single-turn performance and multi-turn
reasoning. We introduce Unary Feedback as Observation (UFO) for reinforcement
learning, which uses minimal yet common unary user feedback during iterative
problem solving. It can be easily applied to existing single-turn RL training
setups. Experimental results show that RL training with UFO keeps single-turn
performance and improves multi-turn reasoning accuracy by up to 14%, enabling
language models to better react to feedback in multi-turn problem solving. To
further minimize the number of turns needed for a correct answer while
encouraging diverse reasoning when mistakes occur, we design reward structures
that guide models to produce careful and deliberate answers in each turn. Code:
https://github.com/lichengliu03/unary-feedback