9 papers
TRAM: Enhancing Multimodal Reasoning with Trajectory-Derived Auxiliary Memory
Kang Liu, Zijing Wang, Yongkang Liu +5
Multimodal Large Reasoning Models (MLRMs) have achieved strong performance on tasks requiring visual understanding and multi-step inference. However, as reasoning trajectories grow…
Generalizable End-to-End Tool-Use RL with Synthetic CodeGym
Weihua Du, Hailei Gong, Zhan Ling +7
Tool-augmented large language models (LLMs), hereafter LLM agents, leverage external tools to solve diverse tasks and interface with the real world. However, current training pract…
NEAT: Neuron-Based Early Exit for Large Reasoning Models
Kang Liu, Yongkang Liu, Xiaocui Yang +5
Large Reasoning Models (LRMs) often suffer from \emph{overthinking}, a phenomenon in which redundant reasoning steps are generated after a correct solution has already been reached…
Natural Language Actor-Critic: Scalable Off-Policy Learning in Language Space
Joey Hong, Kang Liu, Zhan Ling +2
Large language model (LLM) agents -- LLMs that dynamically interact with an environment over long horizons -- have become an increasingly important area of research, enabling autom…
LongReason: A Synthetic Long-Context Reasoning Benchmark via Context Expansion
Zhan Ling, Kang Liu, Kai Yan +6
Large language models (LLMs) have demonstrated remarkable progress in understanding long-context inputs. However, benchmarks for evaluating the long-context reasoning abilities of…
SR-KI: Scalable and Real-Time Knowledge Integration into LLMs via Supervised Attention
Bohan Yu, Wei Huang, Kang Liu
This paper proposes SR-KI, a novel approach for integrating real-time and large-scale structured knowledge bases (KBs) into large language models (LLMs). SR-KI begins by encoding K…