arXiv:2603.00963v2 Announce Type: replace-cross Abstract: While reinforcement learning (RL) has been central to the recent success of large language models (LLMs), RL optimization is notoriously unstable, especially when compared to supervised fine-tuning (SFT). In this work, we investigate the stability gap between SFT and RL from a gradient-based perspective, and show that the convexity of the SFT loss with respect to model logits plays a key role in enabling stable training. Our theoretical analysis demonstrates that this property induces favorable gradient directionality during optimizatio
This research addresses a critical stability challenge in applying reinforcement learning to large language models, a primary method for improving AI performance. The ongoing rapid development of LLMs makes any stability enhancement highly relevant now.
Improved stability in RL for LLMs can accelerate AI development, leading to more robust and reliable advanced AI systems. This could reduce development costs and foster broader application of powerful AI models.
The understanding and potential mitigation of instability in RL training for LLMs could lead to more efficient and predictable optimization processes. This would make it easier to build more capable and less erratic AI models.
- · AI developers
- · Large language model companies
- · AI research institutions
More stable and efficient training of reinforcement learning models for large language models will be realized.
The development cycle for advanced AI models may shorten, and their reliability could increase significantly.
More robust LLMs could enable new applications or accelerate the maturity of existing ones, potentially impacting various sectors through improved AI capabilities.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL