4 citations · 9 across the 13 of their papers we have counts for
14 papers
Self-Evolving Embodied Agents via Skill-Harness Evolution
Peidong Wang, Zhiming Ma, Ying Chang +7
Embodied agents are increasingly built as systems around foundation models, where performance depends not only on model weights but also on the skills, context, action interfaces,…
What are Key Factors for Updates in RL for LLM Reasoning?
Peidong Wang, Demi Wang, Xufang Luo +5
Reinforcement Learning from Verifiable Rewards (RLVR) has emerged as a promising framework for enhancing the reasoning ability of large language models. However, much of the existi…
Rebellious Student: Reversing Teacher Signals for Reasoning Exploration with Self-Distilled RLVR
Jeonghye Kim, Jiwon Jeon, Dongsheng Li +1
Self-distillation has emerged as a powerful framework for post-training LLMs, where a teacher conditioned on extra information guides a student without it, both from the same model…
SortedRL: Accelerating RL Training for LLMs through Online Length-Aware Scheduling
Yiqi Zhang, Huiqiang Jiang, Xufang Luo +7
Scaling reinforcement learning (RL) has shown strong promise for enhancing the reasoning abilities of large language models (LLMs), particularly in tasks requiring long chain-of-th…
Exploratory Memory-Augmented LLM Agent via Hybrid On- and Off-Policy Optimization
Zeyuan Liu, Jeonghye Kim, Xufang Luo +2
Exploration remains the key bottleneck for large language model agents trained with reinforcement learning. While prior methods exploit pretrained knowledge, they fail in environme…
Why Does Self-Distillation (Sometimes) Degrade the Reasoning Capability of LLMs?
Jeonghye Kim, Xufang Luo, Minbeom Kim +5
Self-distillation has emerged as an effective post-training paradigm for LLMs, often improving performance while shortening reasoning traces. However, in mathematical reasoning, we…
Understanding Reasoning in LLMs through Strategic Information Allocation under Uncertainty
Jeonghye Kim, Xufang Luo, Minbeom Kim +3
LLMs often exhibit Aha moments such as self-correction after tokens like "Wait," yet the underlying mechanism remains unclear. Standard LLMs collapse mainly through silent divergen…
Agent Lightning: Train ANY AI Agents with Reinforcement Learning
Xufang Luo, Yuge Zhang, Zhiyuan He +5
We present Agent Lightning, a flexible and extensible framework that enables Reinforcement Learning (RL)-based training of Large Language Models (LLMs) for any AI agent. Unlike exi…
MMInference: Accelerating Pre-filling for Long-Context VLMs via Modality-Aware Permutation Sparse Attention
Yucheng Li, Huiqiang Jiang, Chengruidong Zhang +8
The integration of long-context capabilities with visual understanding unlocks unprecedented potential for Vision Language Models (VLMs). However, the quadratic attention complexit…
VisRL: Intention-Driven Visual Perception via Reinforced Reasoning
Zhangquan Chen, Xufang Luo, Dongsheng Li
Visual understanding is inherently intention-driven - humans selectively focus on different regions of a scene based on their goals. Recent advances in large multimodal models (LMM…
On Memory Construction and Retrieval for Personalized Conversational Agents
Zhuoshi Pan, Qianhui Wu, Huiqiang Jiang +8
To deliver coherent and personalized experiences in long-term conversations, existing approaches typically perform retrieval augmented response generation by constructing memory ba…
SCBench: A KV Cache-Centric Analysis of Long-Context Methods
Yucheng Li, Huiqiang Jiang, Qianhui Wu +8
Long-context LLMs have enabled numerous downstream applications but also introduced significant challenges related to computational and memory efficiency. To address these challeng…
MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention
Huiqiang Jiang, Yucheng Li, Chengruidong Zhang +9
The computational challenges of Large Language Model (LLM) inference remain a significant barrier to their widespread deployment, especially as prompt lengths continue to increase.…
Mitigate Position Bias in Large Language Models via Scaling a Single Dimension
Yijiong Yu, Huiqiang Jiang, Xufang Luo +6
Large Language Models (LLMs) are increasingly applied in various real-world scenarios due to their excellent generalization capabilities and robust generative abilities. However, t…