5 papers
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…
Towards Analyzing and Understanding the Limitations of VAPO: A Theoretical Perspective
Jintian Shao, Yiming Cheng, Hongyi Huang +4
The VAPO framework has demonstrated significant empirical success in enhancing the efficiency and reliability of reinforcement learning for long chain-of-thought (CoT) reasoning ta…
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…
PrefillOnly: An Inference Engine for Prefill-only Workloads in Large Language Model Applications
Kuntai Du, Bowen Wang, Chen Zhang +9
Besides typical generative applications, like ChatGPT, GitHub Copilot, and Cursor, we observe an emerging trend that LLMs are increasingly used in traditional discriminative tasks,…