AI Insight
Researchers developed improved reinforcement learning methods for training AI language models to perform complex, multi-step reasoning tasks by using fine-grained, turn-by-turn reward signals instead of single rewards given only at the end. They tested three reward structures with varying granularity and found that providing rewards after each turn of interaction consistently improved training efficiency and final performance compared to sparse, end-of-task rewards. In question-answering experiments, their approach using Proximal Policy Optimization with per-turn rewards achieved the highest accuracy and most stable training among current state-of-the-art methods.
Why it matters
This work could enable more reliable AI assistants capable of multi-step reasoning for applications like scientific problem-solving, medical diagnosis support, and complex planning tasks. The improved training stability and performance suggests practical pathways for deploying language models in scenarios requiring extended chains of logical reasoning.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
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Abstract: Reinforcement Learning (RL) approaches have been wildly used to enhance the reasoning capabilities of Large Language Model (LLM) agents in long-horizon, multi-turn scenarios. Such interactions can be formalized as turn-level Markov decision processes (MDPs), where intermediate rewards are often available. However, most prior work relies on sparse trajectory-level rewards, resulting in poor credit assignment, while dense turn-level rewards remain underexplored. In this paper, we investigate how to effectively leverage dense turn-level reward structures in RL algorithms, specifically Group Relative Policy Optimization (GRPO) and Proximal Policy Optimization (PPO), to enable fine-grained credit assignment. We categorize reward structures into three types based on their granularity: (1) terminal reward; (2) delayed reward; (3) per-turn reward, each corresponding to a distinct turn-level MDP formulation, and derive GRPO and PPO algorithms tailored to each case, respectively. Experiments on multi-turn search and game agent tasks show that, for both GRPO and PPO, dense per-turn reward structures consistently outperform sparse terminal and delayed reward structures in terms of training dynamics and numerical results. Furthermore, on search tasks, PPO with dense per-turn rewards achieves greater training stability and faster convergence, and delivers the highest answer correctness among all state-of-the-art methods across diverse question-answering datasets.