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Power-Law Decay Loss for Large Language Model Finetuning: A Theory Perspective
Jintian Shao
During the finetuning stage of text generation tasks, standard cross-entropy loss treats all tokens equally. This can lead models to overemphasize high-frequency, low-information t…
Towards Analyzing and Understanding the Limitations of VAPO: A Theoretical Perspective
Jintian Shao, Yiming Cheng
Reinforcement learning (RL) enhances large language models (LLMs) in complex, long-chain-of-thought (long-CoT) reasoning. The advanced VAPO framework, despite sophisticated mechani…
CoT is Not True Reasoning, It Is Just a Tight Constraint to Imitate: A Theory Perspective
Jintian Shao, Yiming Cheng
Chain-of-Thought (CoT) prompting has demonstrably enhanced the performance of Large Language Models on tasks requiring multi-step inference. This success has led to widespread clai…
VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits
Jintian Shao, Hongyi Huang, Jiayi Wu +4
Large Language Models (LLMs) have achieved remarkable success but face significant computational and memory challenges, particularly due to their extensive output vocabularies. The…