A Minimalist Approach to LLM Reasoning: From Rejection Sampling to Reinforce

Fine-tuning large language models (LLMs) for complex reasoning is a central challenge in current AI research. Reinforcement learning (RL) has proven to be a promising approach. In particular, GRPO, an RL method, has achieved considerable success with models like DeepSeek-R1. However, the reasons for GRPO's effectiveness have been little explored so far.

A new study now examines GRPO from the perspective of a Reinforce-like algorithm and analyzes its core components. Surprisingly, it turns out that a simple rejection sampling approach, called RAFT, achieves comparable results to GRPO and PPO. RAFT trains exclusively with positively rated examples.

The researchers' ablation studies reveal that the main advantage of GRPO lies in discarding prompts with completely wrong answers, and not in reward normalization. Based on this finding, the authors propose Reinforce-Rej, a minimal extension of the policy gradient method that filters both completely wrong and completely correct examples. Reinforce-Rej improves KL efficiency and stability and represents a lightweight yet effective alternative to more complex RL algorithms.

The study highlights RAFT as a robust and interpretable baseline and argues that future research should focus on developing more principled methods for incorporating negative examples, rather than using them indiscriminately. The results offer valuable clues for future work in the field of reward-based post-training of LLMs.

The Significance for AI-Powered Content Creation

These research findings are particularly relevant for companies like Mindverse, which offer AI-powered content solutions. Efficient and robust reasoning is crucial for generating high-quality texts, chatbots, voicebots, and AI search engines. The study's insights could help optimize training methods for LLMs and improve the performance of these systems in various application areas. A deeper understanding of how RL algorithms work enables the development of customized solutions that meet the specific needs of customers.

Outlook

The development of efficient and interpretable RL algorithms for LLMs is a dynamic field of research. The presented study provides important impulses for future research and could pave the way for new, more powerful AI systems. In particular, the focus on the principled inclusion of negative examples opens exciting perspectives for the further development of RL methods.

Bibliography: - Xiong, W., Yao, J., Xu, Y., Pang, B., Wang, L., Sahoo, D., Li, J., Jiang, N., Zhang, T., Xiong, C., & Dong, H. (2025). A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce. arXiv preprint arXiv:2504.11343. - Ruvini, J.-D. (2025). LLM Papers Reading Notes - March 2025. LinkedIn. - Li, X. (n.d.). llm-arxiv-daily. GitHub.