Demonstrates that PPO-style clipping and policy ratio constraints are unnecessary for improving reasoning in Large Language Models.
March 20, 2026
Original Paper
Are complicated loss functions necessary for teaching LLMs to reason?
arXiv · 2603.18756
The Takeaway
It simplifies the popular Group Relative Policy Optimization (GRPO) into a cleaner REINFORCE variant (RGRA). For researchers, this suggests that the success of recent 'reasoning' models like DeepSeek-R1 is due to group-based advantage estimation, not the complex constraints of PPO.
From the 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