8 papers · 1 filter
SERO: Learning from Executable Oracle Continuations for Long-Horizon Agentic Reinforcement Learning
Dingyi Rong, Yue Shi, Chaofan Ma +5
Long-horizon agentic RL is constrained by the futures visited by the current policy. When rollouts fail to reach useful behaviors, improved credit assignment cannot provide supervi…
ICAE-Bench: Evaluating Coding Agents as Interactive Project Builders
Zhongyuan Peng, Dan Huang, Chuyu Zhang +8
The recent emergence of vibe-coding workflows is changing what coding agents are expected to do. Instead of merely completing code under fully specified instructions, agents are in…
DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes
Caijun Xu, Changyi Xiao, Zhongyuan Peng +1
Reinforcement learning has become a central paradigm for advancing reasoning in large language models, yet most existing methods still depend on stronger teacher models or heavily…
GAM-RAG: Gain-Adaptive Memory for Evolving Retrieval in Retrieval-Augmented Generation
Yifan Wang, Mingxuan Jiang, Zhihao Sun +5
Retrieval-Augmented Generation (RAG) grounds large language models with external evidence, but many implementations rely on pre-built indices that remain static after construction.…
Retrieval-Infused Reasoning Sandbox: A Benchmark for Decoupling Retrieval and Reasoning Capabilities
Shuangshuang Ying, Zheyu Wang, Yunjian Peng +16
Despite strong performance on existing benchmarks, it remains unclear whether large language models can reason over genuinely novel scientific information. Most evaluations score e…
ARM: Role-Conditioned Neuron Transplantation for Training-Free Generalist LLM Agent Merging
Zhuoka Feng, Kang Chen, Sihan Zhao +7
Interactive large language model agents have advanced rapidly, but most remain specialized to a single environment and fail to adapt robustly to other environments. Model merging o…
SCALER:Synthetic Scalable Adaptive Learning Environment for Reasoning
Caijun Xu, Changyi Xiao, Zhongyuan Peng +2
Reinforcement learning (RL) offers a principled way to enhance the reasoning capabilities of large language models, yet its effectiveness hinges on training signals that remain inf…
Complex Logical Query Answering by Calibrating Knowledge Graph Completion Models
Changyi Xiao, Yixin Cao
Complex logical query answering (CLQA) is a challenging task that involves finding answer entities for complex logical queries over incomplete knowledge graphs (KGs). Previous rese…