15 citations · 29 across the 9 of their papers we have counts for
10 papers
Trust Region Policy Distillation
Zhengpeng Xie, Li Lyna Zhang, Zeke Xie +1
Big goals are hard to achieve all at once; breaking them into small steps is wiser. We present Trust Region Policy Distillation (TOP-D), which transforms the notoriously unstable,…
LoongRL: Reinforcement Learning for Advanced Reasoning over Long Contexts
Siyuan Wang, Gaokai Zhang, Li Lyna Zhang +4
Reasoning over long contexts is essential for large language models. While reinforcement learning (RL) enhances short-context reasoning by inducing "Aha" moments in chain-of-though…
rStar2-Agent: Agentic Reasoning Technical Report
Ning Shang, Yifei Liu, Yi Zhu +12
We introduce rStar2-Agent, a 14B math reasoning model trained with agentic reinforcement learning to achieve frontier-level performance. Beyond current long CoT, the model demonstr…
rStar-Coder: Scaling Competitive Code Reasoning with a Large-Scale Verified Dataset
Yifei Liu, Li Lyna Zhang, Yi Zhu +5
Advancing code reasoning in large language models (LLMs) is fundamentally limited by the scarcity of high-difficulty datasets, especially those with verifiable input-output test ca…
LongRoPE2: Near-Lossless LLM Context Window Scaling
Ning Shang, Li Lyna Zhang, Siyuan Wang +5
LongRoPE2 is a novel approach that extends the effective context window of pre-trained large language models (LLMs) to the target length, while preserving the performance on the or…
rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking
Xinyu Guan, Li Lyna Zhang, Yifei Liu +5
We present rStar-Math to demonstrate that small language models (SLMs) can rival or even surpass the math reasoning capability of OpenAI o1, without distillation from superior mode…
Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers
Zhenting Qi, Mingyuan Ma, Jiahang Xu +3
This paper introduces rStar, a self-play mutual reasoning approach that significantly improves reasoning capabilities of small language models (SLMs) without fine-tuning or superio…
LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens
Yiran Ding, Li Lyna Zhang, Chengruidong Zhang +5
Large context window is a desirable feature in large language models (LLMs). However, due to high fine-tuning costs, scarcity of long texts, and catastrophic values introduced by n…
Fewer is More: Boosting LLM Reasoning with Reinforced Context Pruning
Xijie Huang, Li Lyna Zhang, Kwang-Ting Cheng +2
Large Language Models (LLMs) have shown impressive capabilities, yet they still struggle with math reasoning. In this work, we propose CoT-Influx, a novel approach that pushes the…
Compresso: Structured Pruning with Collaborative Prompting Learns Compact Large Language Models
Song Guo, Jiahang Xu, Li Lyna Zhang +1
Despite the remarkable success of Large Language Models (LLMs), the massive size poses significant deployment challenges, particularly on resource-constrained hardware. While exist…