61 citations · 83 across the 16 of their papers we have counts for
4 papers · 1 filter
DSTC: Direct Preference Learning with Only Self-Generated Tests and Code to Improve Code LMs
Zhihan Liu, Shenao Zhang, Yongfei Liu +3
Direct preference learning offers a promising and computation-efficient beyond supervised fine-tuning (SFT) for improving code generation in coding large language models (LMs). How…
Provably Mitigating Overoptimization in RLHF: Your SFT Loss is Implicitly an Adversarial Regularizer
Zhihan Liu, Miao Lu, Shenao Zhang +5
Aligning generative models with human preference via RLHF typically suffers from overoptimization, where an imperfectly learned reward model can misguide the generative model to ou…
-Puzzle: A Cost-Efficient Testbed for Benchmarking Reinforcement Learning Algorithms in Generative Language Model
Yufeng Zhang, Liyu Chen, Boyi Liu +4
Recent advances in reinforcement learning (RL) algorithms aim to enhance the performance of language models at scale. Yet, there is a noticeable absence of a cost-effective and sta…
Double Duality: Variational Primal-Dual Policy Optimization for Constrained Reinforcement Learning
Zihao Li, Boyi Liu, Zhuoran Yang +2
We study the Constrained Convex Markov Decision Process (MDP), where the goal is to minimize a convex functional of the visitation measure, subject to a convex constraint. Designin…