Strategic Transition from SFT to RL in LLM Development: A Performance-Driven Approach
Published:Jan 9, 2026 09:21
•1 min read
•Zenn LLM
Analysis
This article addresses a crucial aspect of LLM development: the transition from supervised fine-tuning (SFT) to reinforcement learning (RL). It emphasizes the importance of performance signals and task objectives in making this decision, moving away from intuition-based approaches. The practical focus on defining clear criteria for this transition adds significant value for practitioners.
Key Takeaways
- •The transition from SFT to RL in LLM development should be driven by performance signals and task objectives.
- •SFT is responsible for teaching the LLM the format and inference rules.
- •RL focuses on teaching the LLM preferences, safety, and overall quality of responses.
Reference
“SFT: Phase for teaching 'etiquette (format/inference rules)'; RL: Phase for teaching 'preferences (good/bad/safety)'”