256 citations · 285 across the 19 of their papers we have counts for
22 papers
Scaling Verifiable Environments for Long-horizon Work Agents
Jiazheng Zhang, Long Ma, Yunxian Yang +24
Work agents operate over digital artifacts to execute professional knowledge-intensive work, requiring training environments that support long-horizon interaction and trustworthy v…
ReDraft, Don't Just Distill: Reference-Driven Revision for Continual VLLM Post-Training
Zhihao Zhang, Mingqi Wu, Qiaole Dong +15
Continual post-training of large multimodal models should add new capabilities while preserving those from pre-training, and the two goals pull in opposite directions. SFT gives ex…
ContextWeave: A Real-World Workflow Benchmark
Bo Wang, Yuqian Yao, Enxi Wang +25
Memory is essential as language agents move from isolated tasks to long-horizon, stateful workflows, yet existing evaluations often reduce it to retrieval or question answering. We…
MM-Doc-R1: Training Agents for Long Document Visual Question Answering through Multi-turn Reinforcement Learning
Jiahang Lin, Kai Hu, Binghai Wang +12
Conventional Retrieval-Augmented Generation (RAG) systems often struggle with complex multi-hop queries over long documents due to their single-pass retrieval. We introduce MM-Doc-…
JFTA-Bench: Evaluate LLM's Ability of Tracking and Analyzing Malfunctions Using Fault Trees
Yuhui Wang, Zhixiong Yang, Ming Zhang +10
In the maintenance of complex systems, fault trees are used to locate problems and provide targeted solutions. To enable fault trees stored as images to be directly processed by la…
Steering LLMs via Scalable Interactive Oversight
Enyu Zhou, Zhiheng Xi, Long Ma +9
As Large Language Models increasingly automate complex, long-horizon tasks such as \emph{vibe coding}, a supervision gap has emerged. While models excel at execution, users often s…
FRoM-W1: Towards General Humanoid Whole-Body Control with Language Instructions
Peng Li, Zihan Zhuang, Yangfan Gao +16
Humanoid robots are capable of performing various actions such as greeting, dancing and even backflipping. However, these motions are often hard-coded or specifically trained, whic…
Nex-N1: Agentic Models Trained via a Unified Ecosystem for Large-Scale Environment Construction
AGI Team, Yuxuan Cai, Lu Chen +62
The evolution of Large Language Models (LLMs) from passive responders to autonomous agents necessitates a fundamental shift in learning paradigms -- from static imitation to incent…
BAPO: Stabilizing Off-Policy Reinforcement Learning for LLMs via Balanced Policy Optimization with Adaptive Clipping
Zhiheng Xi, Xin Guo, Yang Nan +18
Reinforcement learning (RL) has recently become the core paradigm for aligning and strengthening large language models (LLMs). Yet, applying RL in off-policy settings--where stale…
VRPO: Rethinking Value Modeling for Robust RL under Noisy Supervision in LLM Post-Training
Dingwei Zhu, Shihan Dou, Zhiheng Xi +16
Reinforcement Learning (RL) in real-world environments often suffers from ambiguous or incomplete reward supervision, which undermines policy stability and generalization. Such noi…
Pre-Trained Policy Discriminators are General Reward Models
Shihan Dou, Shichun Liu, Yuming Yang +19
We offer a novel perspective on reward modeling by formulating it as a policy discriminator, which quantifies the difference between two policies to generate a reward signal, guidi…
Why Reinforcement Fine-Tuning Enables MLLMs Preserve Prior Knowledge Better: A Data Perspective
Zhihao Zhang, Qiaole Dong, Qi Zhang +12
Post-training algorithms such as Supervised Fine-Tuning (SFT) and Reinforcement Fine-Tuning (RFT) are widely used to adapt (multimodal) large language models to downstream tasks. W…
RMB: Comprehensively Benchmarking Reward Models in LLM Alignment
Enyu Zhou, Guodong Zheng, Binghai Wang +11
Reward models (RMs) guide the alignment of large language models (LLMs), steering them toward behaviors preferred by humans. Evaluating RMs is the key to better aligning LLMs. Howe…
What's Wrong with Your Code Generated by Large Language Models? An Extensive Study
Shihan Dou, Haoxiang Jia, Shenxi Wu +14
The increasing development of LLMs in code generation has drawn significant attention among researchers. To enhance LLM-based code generation ability, current efforts are predomina…
Aligning Large Language Models from Self-Reference AI Feedback with one General Principle
Rong Bao, Rui Zheng, Shihan Dou +6
In aligning large language models (LLMs), utilizing feedback from existing advanced AI rather than humans is an important method to scale supervisory signals. However, it is highly…
SafeAligner: Safety Alignment against Jailbreak Attacks via Response Disparity Guidance
Caishuang Huang, Wanxu Zhao, Rui Zheng +11
As the development of large language models (LLMs) rapidly advances, securing these models effectively without compromising their utility has become a pivotal area of research. How…
MetaRM: Shifted Distributions Alignment via Meta-Learning
Shihan Dou, Yan Liu, Enyu Zhou +9
The success of Reinforcement Learning from Human Feedback (RLHF) in language model alignment is critically dependent on the capability of the reward model (RM). However, as the tra…
StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback
Shihan Dou, Yan Liu, Haoxiang Jia +14
The advancement of large language models (LLMs) has significantly propelled the field of code generation. Previous work integrated reinforcement learning (RL) with compiler feedbac…
Secrets of RLHF in Large Language Models Part II: Reward Modeling
Binghai Wang, Rui Zheng, Lu Chen +24
Reinforcement Learning from Human Feedback (RLHF) has become a crucial technology for aligning language models with human values and intentions, enabling models to produce more hel…
LoRAMoE: Alleviate World Knowledge Forgetting in Large Language Models via MoE-Style Plugin
Shihan Dou, Enyu Zhou, Yan Liu +13
Supervised fine-tuning (SFT) is a crucial step for large language models (LLMs), enabling them to align with human instructions and enhance their capabilities in downstream tasks.…
RealBehavior: A Framework for Faithfully Characterizing Foundation Models' Human-like Behavior Mechanisms
Enyu Zhou, Rui Zheng, Zhiheng Xi +7
Reports of human-like behaviors in foundation models are growing, with psychological theories providing enduring tools to investigate these behaviors. However, current research ten…
The Rise and Potential of Large Language Model Based Agents: A Survey
Zhiheng Xi, Wenxiang Chen, Xin Guo +26
For a long time, humanity has pursued artificial intelligence (AI) equivalent to or surpassing the human level, with AI agents considered a promising vehicle for this pursuit. AI a…