1 citations · 1 across the 15 of their papers we have counts for
16 papers
EvalBuilder: Can LLMs Build Efficient Benchmarks?
Huaisheng Zhu, Hangfan Zhang, Teng Xiao +5
Large language model (LLM) agents are increasingly used for long-horizon research and engineering, yet evaluating these capabilities remains expensive, time-consuming, and heavily…
What Transfers from Text to Vision? Capability Scaling Laws and Transfer Dynamics for VLMs
Ziran Li, Qiang Wang, Zhengyu Chen +4
Choosing the right large language model (LLM) backbone is the most consequential decision when building a vision-language model (VLM), yet it remains fundamentally unprincipled: co…
Beyond Final Scores: A Systematic Evaluation of Agents for Long-Horizon AI Research and Development
Yiwei Li, Wanli Yang, Hexiang Tan +10
Autonomous agents are increasingly capable of improving models, systems, and other technical artifacts through long-horizon experimentation. To understand the current state of this…
Distilling Temporal Search and Reasoning: Evolving LLMs for Future Prediction via Harness-Assisted Efficient Data Synthesis
Wanxu Cai, Zhengyu Chen, Huaisheng Zhu +3
Future event prediction carries broad social impact yet remains challenging. SOTA approaches augment LLMs with external agent frameworks whose predictive capability vanishes once t…
Co-Harness: Co-Evolving Harnesses and Model Weights for LLM Agents
Zhengyu Chen, Teng Xiao, Huaisheng Zhu +3
Post-training agents for automated AI research requires optimizing not only model parameters, but also the runtime harness that shapes how research trajectories are generated, eval…
ToFu: A White-Box, Token-Efficient Agent Harness for Researchers
Junhao Ruan, Yuan Ge, Bei Li +7
Agentic coding tools present new opportunities to transform research workflows. The performance of agent systems built depends on both large language models (LLMs) and the harness…
Beyond Trajectory Imitation: Strategy-Guided Policy Optimization for LLM Reasoning
Tianyuan Shi, Canbin Huang, Bei Li +4
Distilling reasoning capabilities from strong to weak language models typically involves imitating specific solution trajectories, effectively transferring what to answer rather th…
Teacher-Guided Policy Optimization for On-Policy Reasoning Distillation under Large Policy Divergence
Xinyu Liu, Kechen Jiao, Chunyang Xiao +10
On-policy distillation (OPD) has become a promising paradigm for reasoning-oriented post-training of large language models (LLMs), especially when combined with reinforcement learn…
LANG: Reinforcement Learning for Multilingual Reasoning with Language-Adaptive Hint Guidance
Yuchun Fan, Bei Li, Peiguang Li +9
Reinforcement learning has proven effective for enhancing multi-step reasoning in large language models (LLMs), yet its benefits have not fully translated to multilingual contexts.…
MTR-Suite: A Framework for Evaluating and Synthesizing Conversational Retrieval Benchmarks
Junhao Ruan, Abudukeyumu Abudula, Bei Li +8
Accurate evaluation of conversational retrieval is pivotal for advancing Retrieval-Augmented Generation (RAG) systems. However, existing conversational retrieval benchmarks suffer…
Multi-Objective and Mixed-Reward Reinforcement Learning via Reward-Decorrelated Policy Optimization
Yang Bai, Kaiyuan Liu, Ziyuan Zhuang +5
Complex reinforcement learning environments frequently employ multi-task and mixed-reward formulations. In these settings, heterogeneous reward distributions and correlated reward…
LongCat-Flash-Prover: Advancing Native Formal Reasoning via Agentic Tool-Integrated Reinforcement Learning
Jianing Wang, Jianfei Zhang, Qi Guo +24
We introduce LongCat-Flash-Prover, a flagship 560-billion-parameter open-source Mixture-of- Experts (MoE) model that advances Native Formal Reasoning in Lean4 through agentic tool-…
Scaling and Transferability of Annealing Strategies in Large Language Model Training
Siqi Wang, Zhengyu Chen, Teng Xiao +5
Learning rate scheduling is crucial for training large language models, yet understanding the optimal annealing strategies across different model configurations remains challenging…
Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs
Zhengyu Chen, Siqi Wang, Teng Xiao +5
Traditional scaling laws in natural language processing suggest that increasing model size and training data enhances performance. However, recent studies reveal deviations, partic…
From Mathematical Reasoning to Code: Generalization of Process Reward Models in Test-Time Scaling
Zhengyu Chen, Yudong Wang, Teng Xiao +5
Recent advancements in improving the reasoning capabilities of Large Language Models have underscored the efficacy of Process Reward Models (PRMs) in addressing intermediate errors…
Scaling Laws Across Model Architectures: A Comparative Analysis of Dense and MoE Models in Large Language Models
Siqi Wang, Zhengyu Chen, Bei Li +3
The scaling of large language models (LLMs) is a critical research area for the efficiency and effectiveness of model training and deployment. Our work investigates the transferabi…