5 papers
BridgeAlign: Bridging Preference Alignment for Humanities and Social Sciences
Ru Peng, Haokai Xu, Xijun Gu +11
While data synthesis for large language models (LLMs) is prevalent, it primarily targets domains with verifiable answers, overlooking open-ended humanities and social sciences (HSS…
Can LLM design high-quality experiments? A Comprehensive and Systematic Benchmark on Autonomous Experimental Design
Zejun Liu, Jian Wu, Ru Peng +4
AI for Research (AI4Research) leverages AI to automate and improve scientific workflows. While experimental design is a critical stage of the research process, prior work has focus…
HSS-Synth: Humanities and Social Sciences Data Synthesis for LLMs
Ru Peng, Tianyu Zhao, Xijun Gu +9
High-quality, diverse data are vital for large language models (LLMs) but remain scarce and costly. Data synthesis is a viable alternative and succeeds on closed tasks, yet the hum…
Momentum for Reasoning: Dense Intrinsic Signals in Policy Optimization
Hao Chen, Zhanming Shen, Liyao Li +8
Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful paradigm for eliciting long-chain reasoning in large language models. However, existing methods base…
Optimsyn: Influence-Guided Rubrics Optimization for Synthetic Data Generation
Zhiting Fan, Ruizhe Chen, Tianxiang Hu +7
Large language models (LLMs) achieve strong downstream performance largely due to abundant supervised fine-tuning (SFT) data. However, high-quality SFT data in knowledge-intensive…