Title: Are complicated loss functions necessary for teaching LLMs to reason?
Primary area: reinforcement learning
Keywords: LLMs, Reinforcement Learning, Reasoning

Abstract: Recent advances in large language models (LLMs) highlight the importance of post-training techniques for improving reasoning and mathematical ability. Group Relative Policy Optimization (GRPO) has shown promise in this domain by combining group-relative advantage estimation, PPO-style clipping, and KL regularization. However, its complexity raises the question of whether all components are necessary for fostering reasoning behaviors. We conduct a systematic analysis of GRPO and identify two key findings: (1) incorporating negative feedback is essential—training solely on actions above a baseline limits learning; and (2) PPO-style constraints, such as policy ratio clipping, are not required to improve mathematical reasoning or performance. Building on these insights, we propose REINFORCE with Group Relative Advantage (RGRA), a simplified variant that retains group-relative advantage estimation but removes PPO-style clipping and policy ratio terms. Experiments across standard mathematical benchmarks indicate that RGRA has the potential to achieve stronger performance than GRPO. Our results suggest that simpler REINFORCE-based approaches can effectively enhance reasoning in LLMs, offering a more transparent and efficient alternative to GRPO.