5 papers
CodeDPO: Aligning Code Models with Self Generated and Verified Source Code
Kechi Zhang, Ge Li, Yihong Dong +5
Code generation models have shown significant potential for programming tasks. However, existing training methods like supervised fine-tuning face key limitations: they do not effe…
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…
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…
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…