18 papers
HIPIF: Hierarchical Planning and Information Folding for Long-Horizon LLM Agent Learning
Juncheng Diao, Zhicong Lu, Peiguang Li +6
While Large Language Models (LLMs) have demonstrated strong capabilities as autonomous agents across a wide range of tasks, their performance often degrades in multi-turn long-hori…
STAGE-Claw: Automated State-based Agent Benchmarking for Realistic Scenarios
Sirui Liang, Bohan Yu, Peiyu Wang +8
Large language models are increasingly used to power personal agents for everyday applications, but evaluating these agents remains a challenge. Existing benchmarks still rely on s…
SIRI: Self-Internalizing Reinforcement Learning with Intrinsic Skills for LLM Agent Training
Zhongyu He, Yuanfan Li, Fei Huang +9
Long-horizon LLM agents can benefit from reusable skills, yet existing skill-based methods often rely on external skill generators during training or persistent skill retrieval at…
Asymmetric On-Policy Distillation: Bridging Exploitation and Imitation at the Token Level
Nan Jia, Haojin Yang, Xing Ma +6
On-policy distillation (OPD) trains a student on its own trajectories with token-level teacher feedback and often outperforms off-policy distillation and standard reinforcement lea…
Skill1: Unified Evolution of Skill-Augmented Agents via Reinforcement Learning
Yaorui Shi, Yuxin Chen, Zhengxi Lu +6
A persistent skill library allows language model agents to reuse successful strategies across tasks. Maintaining such a library requires three coupled capabilities. The agent selec…
How to Allocate, How to Learn? Dynamic Rollout Allocation and Advantage Modulation for Policy Optimization
Yangyi Fang, Jiaye Lin, Xiaoliang Fu +6
Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective for Large Language Model (LLM) reasoning, yet current methods face key challenges in resource allocation…