4 citations · 4 across the 14 of their papers we have counts for
15 papers
CodeMidas: Scaling Agentic Coding RL Environments from Code Itself
Bowen Ye, Lei Li, Shicheng Li +16
Training capable coding agents via reinforcement learning (RL) requires diverse tasks with reliable verifiers. Open-source codebases offer a rich source of such tasks, while existi…
Practice Makes Unsafe: Skill Misevolution in Self-Improving LLM Agents
Xutao Mao, Liangjie Zhao, Xiang Zheng +1
LLM agents increasingly save experience from past tasks as reusable skills through skill evolution. When an agent completes an unsafe task, skill evolution can keep the unsafe proc…
From Refuse to Richness: Rubric Rewards for Long-Form Hallucination Reinforcement Learning
Yudong Wang, Zhe Yang, Wenhan Ma +6
Rewards that penalize unsupported claims can improve grounding in long-form generation, but they can also teach models to answer less. We study this refusal-to-richness trade-off i…
Agents Don't Just Agree, They Remember: Benchmarking Persistent Sycophancy in Self-Improving Personal Agents
Xutao Mao, Liangjie Zhao, Leyao Wang +6
Self-improving personal agents write profiles, memories, and reusable skills that carry over from one chat to the next. Prior works ask whether a model agrees with the user in its…
MOPD: Multi-Teacher On-Policy Distillation for Capability Integration in LLM Post-Training
Wenhan Ma, Jianyu Wei, Liang Zhao +10
Modern large language models (LLMs) rely on reinforcement learning during post-training to push specific capabilities, yet integrating multiple capabilities into one model remains…
SkillOps: Managing LLM Agent Skill Libraries as Self-Maintaining Software Ecosystems
Hongji Pu, Xinyuan Song, Liang Zhao
Large language model agents increasingly rely on skill libraries for multi-step tasks, yet these libraries can accumulate persistent defects as skills are added, reused, patched, a…
MolWorld: Molecule World Models for Actionable Molecular Optimization
Yang Qiao, Bo Pan, Hao-Wei Pang +3
Molecular optimization in drug discovery aims to discover molecules with improved target properties, but practical lead optimization often requires more than high predicted scores.…
CoCoDA: Co-evolving Compositional DAG for Tool-Augmented Agents
Ziyang Yu, Qiyue Li, Liang Zhao
Tool-augmented language models can extend small language models with external executable skills, but scaling the tool library creates a coupled challenge: the library must evolve w…
SkillLens: Adaptive Multi-Granularity Skill Reuse for Cost-Efficient LLM Agents
Yongliang Miao, Ziyang Yu, Liang Zhao +2
Skill libraries have become a practical way for LLM agents to reuse procedural experience across tasks. However, existing systems typically treat skills as flat, single-resolution…
STARE: Step-wise Temporal Alignment and Red-teaming Engine for Multi-modal Toxicity Attack
Xutao Mao, Liangjie Zhao, Tao Liu +3
Red-teaming Vision-Language Models is essential for identifying vulnerabilities where adversarial image-text inputs trigger toxic outputs. Existing approaches treat image generatio…
ARL-Tangram: Unleash the Resource Efficiency in Agentic Reinforcement Learning
Bangjun Xiao, Yihao Zhao, Xiangwei Deng +9
Agentic reinforcement learning (RL) has emerged as a transformative workload in cloud clusters, enabling large language models (LLMs) to solve complex problems through interactions…
Reinforcement Learning for Chain of Thought Compression with One-Domain-to-All Generalization
Hanyu Li, Jiangshan Duo, Bofei Gao +4
Chain-of-thought reasoning in large language models can trigger an "overthinking trap": longer rollouts raise cost and latency yet often yield unreliable accuracy gains. Existing m…
JudgeRLVR: Judge First, Generate Second for Efficient Reasoning
Jiangshan Duo, Hanyu Li, Hailin Zhang +3
Reinforcement Learning with Verifiable Rewards (RLVR) has become a standard paradigm for reasoning in Large Language Models. However, optimizing solely for final-answer correctness…
Enhancing Reliability across Short and Long-Form QA via Reinforcement Learning
Yudong Wang, Zhe Yang, Wenhan Ma +2
While reinforcement learning has unlocked unprecedented complex reasoning in large language models, it has also amplified their propensity for hallucination, creating a critical tr…
Stabilizing MoE Reinforcement Learning by Aligning Training and Inference Routers
Wenhan Ma, Hailin Zhang, Liang Zhao +4
Reinforcement learning (RL) has emerged as a crucial approach for enhancing the capabilities of large language models. However, in Mixture-of-Experts (MoE) models, the routing mech…