3 citations · 5 across the 8 of their papers we have counts for
6 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…
FullStack Bench: Evaluating LLMs as Full Stack Coders
Bytedance-Seed-Foundation-Code-Team, :, Yao Cheng +53
As the capabilities of code large language models (LLMs) continue to expand, their applications across diverse code intelligence domains are rapidly increasing. However, most exist…
Reward-Augmented Data Enhances Direct Preference Alignment of LLMs
Shenao Zhang, Zhihan Liu, Boyi Liu +6
Preference alignment in Large Language Models (LLMs) has significantly improved their ability to adhere to human instructions and intentions. However, existing direct alignment alg…
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…
How Can LLM Guide RL? A Value-Based Approach
Shenao Zhang, Sirui Zheng, Shuqi Ke +6
Reinforcement learning (RL) has become the de facto standard practice for sequential decision-making problems by improving future acting policies with feedback. However, RL algorit…