52 citations · 155 across the 26 of their papers we have counts for
27 papers
EAU: Environment-Agent-User Co-evolution for Training User-Centric Agents
Min Cai, Dan Zhang, Gao Xing +3
Training LLM agents to interact with users over multiple turns is challenging because the learning signal depends not only on the agent, but also on the user simulator and environm…
Detecting and Suppressing Reward Hacking with Gradient Fingerprints
Songtao Wang, Quang Hieu Pham, Fangcong Yin +4
Reinforcement learning with verifiable rewards (RLVR) typically optimizes for outcome rewards without imposing constraints on intermediate reasoning. This leaves training susceptib…
Agentic Aggregation for Parallel Scaling of Long-Horizon Agentic Tasks
Yoonsang Lee, Howard Yen, Xi Ye +1
We study parallel test-time scaling for long-horizon agentic tasks such as agentic search and deep research, where multiple rollouts are generated in parallel and aggregated into a…
Advancing General-Purpose Reasoning Models with Modular Gradient Surgery
Min Cai, Yu Liang, Longzheng Wang +6
Reinforcement learning (RL) has played a central role in recent advances in large reasoning models (LRMs), yielding strong gains in verifiable and open-ended reasoning. However, tr…
DySCO: Dynamic Attention-Scaling Decoding for Long-Context Language Models
Xi Ye, Wuwei Zhang, Fangcong Yin +2
Understanding and reasoning over long contexts is a crucial capability for language models (LMs). Although recent models support increasingly long context windows, their accuracy o…
Language Models that Think, Chat Better
Adithya Bhaskar, Xi Ye, Danqi Chen
Reinforcement learning with verifiable rewards (RLVR) trains language models to use long chain-of-thought reasoning (CoT) in domains like mathematics and code with rule-based verif…