Supervised Fine-Tuning (SFT) suffers from severe task conflicts during multi-stage training, while Reinforcement Learning (RL) allows tasks to coexist by inducing sparse, nearly orthogonal parameter updates. Interference in SFT scales with absolute gradient magnitude, whereas RL interference is bounded by gradient variance.
HOW THIS AFFECTS YOU
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builderThis suggests RL is a more stable path for scaling reasoning capabilities across diverse tasks.
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researcherYou can leverage RL to mitigate gradient interference in multi-task LLM training.