Study: Multi-stage SFT Degrades LLM Reasoning, Proposes Parallel-RL
burny_tech · x · 2026-08-11
A new analysis reveals that multi-stage Supervised Fine-Tuning (SFT) introduces task conflicts, thereby degrading an LLM's reasoning capabilities across different tasks.
In contrast, Reinforcement Learning (RL) updates remain sparse and near-orthogonal, allowing multi-task updates to coexist without interference. Based on these findings, researchers propose Parallel-RL, a decoupled multi-task training paradigm.
Related event: Study Reveals Multi-stage SFT Causes Task Conflicts, Favoring RL(4 posts)→
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