1 citations · 2 across the 7 of their papers we have counts for
7 papers
StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason
Kaiyi Zhang, Ang Lv, Jinpeng Li +4
Reinforcement learning with verifiable rewards (RLVR) is a promising approach for improving the complex reasoning abilities of large language models (LLMs). However, current RLVR m…
The Climb Carves Wisdom Deeper Than the Summit: On the Noisy Rewards in Learning to Reason
Ang Lv, Ruobing Xie, Xingwu Sun +2
Recent studies on post-training large language models (LLMs) for reasoning through reinforcement learning (RL) typically focus on tasks that can be accurately verified and rewarded…
PolarQuant: Leveraging Polar Transformation for Efficient Key Cache Quantization and Decoding Acceleration
Songhao Wu, Ang Lv, Xiao Feng +5
The KV cache in large language models is a dominant factor in memory usage, limiting their broader applicability. Quantizing the cache to lower bit widths is an effective way to re…
Autonomy-of-Experts Models
Ang Lv, Ruobing Xie, Yining Qian +5
Mixture-of-Experts (MoE) models mostly use a router to assign tokens to specific expert modules, activating only partial parameters and often outperforming dense models. We argue t…
More Expressive Attention with Negative Weights
Ang Lv, Ruobing Xie, Shuaipeng Li +5
We propose a novel attention mechanism, named Cog Attention, that enables attention weights to be negative for enhanced expressiveness, which stems from two key factors: (1) Cog At…
PEAR: Position-Embedding-Agnostic Attention Re-weighting Enhances Retrieval-Augmented Generation with Zero Inference Overhead
Tao Tan, Yining Qian, Ang Lv +7
Large language models (LLMs) enhanced with retrieval-augmented generation (RAG) have introduced a new paradigm for web search. However, the limited context awareness of LLMs degrad…
Language Models "Grok" to Copy
Ang Lv, Ruobing Xie, Xingwu Sun +2
We examine the pre-training dynamics of language models, focusing on their ability to copy text from preceding context--a fundamental skill for various LLM applications, including…