arXiv:2602.01058v2 Announce Type: replace Abstract: Post-training of reasoning LLMs is a holistic process that typically consists of an offline SFT stage followed by an online reinforcement learning (RL) stage. However, SFT is often optimized in isolation to maximize SFT performance alone. We show that, after identical RL training, models initialized from stronger SFT checkpoints can significantly underperform those initialized from weaker ones. We attribute this to a mismatch typical in current SFT-RL pipelines: the distribution that generates the offline SFT data can differ substantially fro
This paper addresses a fundamental challenge emerging as advanced LLMs move from supervised fine-tuning (SFT) to reinforcement learning (RL) for enhanced reasoning capabilities.
It reveals that optimizing SFT in isolation can hinder downstream RL performance, highlighting a critical methodological gap in current AI development pipelines.
The understanding that SFT must be designed not just for immediate performance but also for its suitability as a foundation for subsequent RL training.
- · AI researchers focusing on RL and SFT integration
- · Companies developing advanced reasoning LLMs
- · Hardware providers supporting complex model training
- · AI labs with isolated SFT and RL workflows
- · Developers solely focused on maximizing SFT benchmarks
More integrated and holistic approaches to LLM post-training will gain traction.
New metrics and benchmarks may emerge that evaluate SFT's 'RL-readiness' rather than just its standalone performance.
The development of LLMs for complex, real-world reasoning tasks could accelerate by optimizing the SFT-RL transition.
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Read at arXiv cs.LG