3 citations · 8 across the 18 of their papers we have counts for
10 papers · 1 filter
Youtu-Agent: Scaling Agent Productivity with Automated Generation and Hybrid Policy Optimization
Yuchen Shi, Yuzheng Cai, Siqi Cai +15
Existing Large Language Model (LLM) agent frameworks face two significant challenges: high configuration costs and static capabilities. Building a high-quality agent often requires…
LTD-Bench: Evaluating Large Language Models by Letting Them Draw
Liuhao Lin, Ke Li, Zihan Xu +5
Current evaluation paradigms for large language models (LLMs) represent a critical blind spot in AI research--relying on opaque numerical metrics that conceal fundamental limitatio…
Count Counts: Motivating Exploration in LLM Reasoning with Count-based Intrinsic Rewards
Xuan Zhang, Ruixiao Li, Zhijian Zhou +7
Reinforcement Learning (RL) has become a compelling way to strengthen the multi step reasoning ability of Large Language Models (LLMs). However, prevalent RL paradigms still lean o…
CUARewardBench: A Benchmark for Evaluating Reward Models on Computer-using Agent
Haojia Lin, Xiaoyu Tan, Yulei Qin +9
Computer-using agents (CUAs) enable task completion through natural interaction with operating systems and software interfaces. While script-based verifiers are widely adopted for…
Training-Free Group Relative Policy Optimization
Yuzheng Cai, Siqi Cai, Yuchen Shi +10
Recent advances in Large Language Model (LLM) agents have demonstrated their promising general capabilities. However, their performance in specialized real-world domains often degr…
RoRecomp: Enhancing Reasoning Efficiency via Rollout Response Recomposition in Reinforcement Learning
Gang Li, Yulei Qin, Xiaoyu Tan +6
Reinforcement learning with verifiable rewards (RLVR) has proven effective in eliciting complex reasoning in large language models (LLMs). However, standard RLVR training often lea…
Learn the Ropes, Then Trust the Wins: Self-imitation with Progressive Exploration for Agentic Reinforcement Learning
Yulei Qin, Xiaoyu Tan, Zhengbao He +13
Reinforcement learning (RL) is the dominant paradigm for sharpening strategic tool use capabilities of LLMs on long-horizon, sparsely-rewarded agent tasks, yet it faces a fundament…
Incentivizing Reasoning for Advanced Instruction-Following of Large Language Models
Yulei Qin, Gang Li, Zongyi Li +6
Existing large language models (LLMs) face challenges of following complex instructions, especially when multiple constraints are present and organized in paralleling, chaining, an…
FlowAgent: Achieving Compliance and Flexibility for Workflow Agents
Yuchen Shi, Siqi Cai, Zihan Xu +7
The integration of workflows with large language models (LLMs) enables LLM-based agents to execute predefined procedures, enhancing automation in real-world applications. Tradition…
LUCY: Linguistic Understanding and Control Yielding Early Stage of Her
Heting Gao, Hang Shao, Xiong Wang +12
The film Her features Samantha, a sophisticated AI audio agent who is capable of understanding both linguistic and paralinguistic information in human speech and delivering real-ti…