6 papers
InsightSR: Refining Symbolic Regression Search Spaces via Parallel Semantic and Structural LLM Guidance
Yating Ling, Wenjing Cun, Zhitang Chen
Symbolic regression (SR) seeks to discover parsimonious mathematical laws from observational data, yet conventional approaches often struggle with the vast combinatorial search spa…
ScienceFlow: A long-horizon agent for ML research, scientific discovery and beyond
Mingming Zhao, Jiqian Dong, Kangping Xu +16
Enabling LLM agents to sustain productive, stable, and goal-aligned research over extended horizons is a central challenge for autonomous machine learning and scientific discovery,…
Hista and Numca: Estimate State Value Effectively for LLM Reinforcement Learning
Zizhe Chen, Jiqian Dong, Yizhou Tian +4
Reinforcement learning (RL) refines large language models (LLMs) by directly optimizing model behavior through reward signals. While accurate state value estimation is critical for…
Proximalized Preference Optimization for Diverse Feedback Types: A Decomposed Perspective on DPO
Kaiyang Guo, Yinchuan Li, Zhitang Chen
Direct alignment methods typically train large language models (LLMs) by contrasting the likelihoods of preferred and dispreferred responses. While effective at capturing relative…
Generative Models in Decision Making: A Survey
Xinyu Shao, Jianping Zhang, Haozhi Wang +9
Generative models have fundamentally reshaped the landscape of decision-making, reframing the problem from pure scalar reward maximization to high-fidelity trajectory generation an…
Boosting Cross-problem Generalization in Diffusion-Based Neural Combinatorial Solver via Inference Time Adaptation
Haoyu Lei, Kaiwen Zhou, Yinchuan Li +2
Diffusion-based Neural Combinatorial Optimization (NCO) has demonstrated effectiveness in solving NP-complete (NPC) problems by learning discrete diffusion models for solution gene…