1 citations · 1 across the 11 of their papers we have counts for
12 papers
DRIFT: Decoupled Rollouts and Importance-Weighted Fine-Tuning for Efficient Multi-Turn Optimization
Jian Mu, Tianyi Lin, Chengwei Qin +2
Large language models are increasingly deployed in multi-turn interactive settings where users or environments can iteratively provide lightweight feedback. Unfortunately, optimizi…
Learning Query-Aware Budget-Tier Routing for Runtime Agent Memory
Haozhen Zhang, Haodong Yue, Tao Feng +8
Memory is increasingly central to Large Language Model (LLM) agents operating beyond a single context window, yet most existing systems rely on offline, query-agnostic memory const…
BranPO: Scalable Contrastive Branch Sampling for Long-Horizon Agentic Reinforcement Learning
Yubao Zhao, Weiquan Huang, Sudong Wang +4
Agentic reinforcement learning enables large language models to perform multi-turn planning and tool use, but long-horizon training remains challenging under sparse trajectory-leve…
ReXMoE: Reusing Experts with Minimal Overhead in Mixture-of-Experts
Zheyue Tan, Zhiyuan Li, Tao Yuan +13
Mixture-of-Experts (MoE) architectures have emerged as a promising approach to scale Large Language Models (LLMs). MoE boosts the efficiency by activating a subset of experts per t…
Video-STR: Reinforcing MLLMs in Video Spatio-Temporal Reasoning with Relation Graph
Wentao Wang, Heqing Zou, Tianze Luo +8
Recent progress in Multimodal Large Language Models (MLLMs) has demonstrated strong semantic understanding capabilities, but struggles to perform precise spatio-temporal understand…
FURINA: A Fully Customizable Role-Playing Benchmark via Scalable Multi-Agent Collaboration Pipeline
Haotian Wu, Shufan Jiang, Chios Chen +5
As large language models (LLMs) advance in role-playing (RP) tasks, existing benchmarks quickly become obsolete due to their narrow scope, outdated interaction paradigms, and limit…
Self-Reflective Generation at Test Time
Jian Mu, Qixin Zhang, Zhiyong Wang +5
Large language models (LLMs) increasingly solve complex reasoning tasks via long chain-of-thought, but their forward-only autoregressive generation process is fragile; early token…
RIMO: An Easy-to-Evaluate, Hard-to-Solve Olympiad Benchmark for Advanced Mathematical Reasoning
Ziye Chen, Chengwei Qin, Yao Shu
As large language models (LLMs) reach high scores on established mathematical benchmarks, such as GSM8K and MATH, the research community has turned to International Mathematical Ol…
Implicit Reasoning in Large Language Models: A Comprehensive Survey
Jindong Li, Yali Fu, Li Fan +6
Large Language Models (LLMs) have demonstrated strong generalization across a wide range of tasks. Reasoning with LLMs is central to solving multi-step problems and complex decisio…
Thinking with Nothinking Calibration: A New In-Context Learning Paradigm in Reasoning Large Language Models
Haotian Wu, Bo Xu, Yao Shu +2
Reasoning large language models (RLLMs) have recently demonstrated remarkable capabilities through structured and multi-step reasoning. While prior research has primarily focused o…
dots.llm1 Technical Report
Bi Huo, Bin Tu, Cheng Qin +24
Mixture of Experts (MoE) models have emerged as a promising paradigm for scaling language models efficiently by activating only a subset of parameters for each input token. In this…
MTBench: A Multimodal Time Series Benchmark for Temporal Reasoning and Question Answering
Jialin Chen, Aosong Feng, Ziyu Zhao +7
Understanding the relationship between textual news and time-series evolution is a critical yet under-explored challenge in applied data science. While multimodal learning has gain…