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cs.LG2026
On the Non-decoupling of Supervised Fine-tuning and Reinforcement Learning in Post-training
Xueyan Niu, Bo Bai, Wei Han +1
Post-training of large language models routinely interleaves supervised fine-tuning (SFT) with reinforcement learning (RL). These two methods have different objectives: SFT minimiz…
cs.LG2024★ 1 cited
Beyond Scaling Laws: Understanding Transformer Performance with Associative Memory
Xueyan Niu, Bo Bai, Lei Deng +1
Increasing the size of a Transformer does not always lead to enhanced performance. This phenomenon cannot be explained by the empirical scaling laws. Furthermore, the model's enhan…