5 papers
EvaLearn: Quantifying the Learning Capability and Efficiency of LLMs via Sequential Problem Solving
Shihan Dou, Ming Zhang, Chenhao Huang +14
We introduce EvaLearn, a pioneering benchmark designed to evaluate large language models (LLMs) on their learning capability and efficiency in challenging tasks, a critical, yet un…
PAG: Multi-Turn Reinforced LLM Self-Correction with Policy as Generative Verifier
Yuhua Jiang, Yuwen Xiong, Yufeng Yuan +5
Large Language Models (LLMs) have demonstrated impressive capabilities in complex reasoning tasks, yet they still struggle to reliably verify the correctness of their own outputs.…
Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning
ByteDance Seed, :, Jiaze Chen +267
We introduce Seed1.5-Thinking, capable of reasoning through thinking before responding, resulting in improved performance on a wide range of benchmarks. Seed1.5-Thinking achieves 8…
A Unified Pairwise Framework for RLHF: Bridging Generative Reward Modeling and Policy Optimization
Wenyuan Xu, Xiaochen Zuo, Chao Xin +3
Reinforcement Learning from Human Feedback (RLHF) has emerged as a important paradigm for aligning large language models (LLMs) with human preferences during post-training. This fr…
Exploring Data Scaling Trends and Effects in Reinforcement Learning from Human Feedback
Wei Shen, Guanlin Liu, Zheng Wu +5
Reinforcement Learning from Human Feedback (RLHF) is crucial for aligning large language models with human preferences. While recent research has focused on algorithmic improvement…