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Listen "Unifying LLM Post-Training: From SFT and RL to Hybrid Approaches"
Episode Synopsis
This episode of The ML Digest covers the paper “Towards a Unified View of Large Language Model Post-Training” from researchers at Tsinghua University, Shanghai AI Lab, and WeChat AI. The authors argue that seemingly distinct approaches—Supervised Fine-Tuning (SFT) with offline demonstrations and Reinforcement Learning (RL) with online rollouts—are in fact instances of a single optimization process.Link to original paper: https://arxiv.org/pdf/2509.04419
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