8 papers · 1 filter
The Surprising Effectiveness of Negative Reinforcement in LLM Reasoning
Xinyu Zhu, Mengzhou Xia, Zhepei Wei +3
Reinforcement learning with verifiable rewards (RLVR) is a promising approach for training language models (LMs) on reasoning tasks that elicit emergent long chains of thought (CoT…
BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval
Hongjin Su, Howard Yen, Mengzhou Xia +12
Existing retrieval benchmarks primarily consist of information-seeking queries (e.g., aggregated questions from search engines) where keyword or semantic-based retrieval is usually…
SimPO: Simple Preference Optimization with a Reference-Free Reward
Yu Meng, Mengzhou Xia, Danqi Chen
Direct Preference Optimization (DPO) is a widely used offline preference optimization algorithm that reparameterizes reward functions in reinforcement learning from human feedback…
Lory: Fully Differentiable Mixture-of-Experts for Autoregressive Language Model Pre-training
Zexuan Zhong, Mengzhou Xia, Danqi Chen +1
Mixture-of-experts (MoE) models facilitate efficient scaling; however, training the router network introduces the challenge of optimizing a non-differentiable, discrete objective.…
Language Models as Science Tutors
Alexis Chevalier, Jiayi Geng, Alexander Wettig +19
NLP has recently made exciting progress toward training language models (LMs) with strong scientific problem-solving skills. However, model development has not focused on real-life…
CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs
Zirui Wang, Mengzhou Xia, Luxi He +10
Chart understanding plays a pivotal role when applying Multimodal Large Language Models (MLLMs) to real-world tasks such as analyzing scientific papers or financial reports. Howeve…