Performance, Efficiency and Collapse -- Advantages and Challenges in Offline Post-training of Code LLMs
TL;DR
The findings indicate that, with only a few hours of training, zero-shot code generation performance of LLMs can be substantially improved without online sampling and offline RL produces performance gains across models, although the extent of improvement varies among model families.
Abstract
Post-training with reinforcement learning (RL) is a critical phase in the development of code-generating large language models (LLMs), as it ensures adherence to instructions and the production of functionally correct code. This process typically requires computationally intensive code sample generation from Transformer-based LLMs and substantial GPU-CPU communication for sequence verification. To address these computational challenges, this work examines whether RL-based post-training can be performed entirely offline by leveraging existing datasets rather than generating new samples. The findings indicate that, with only a few hours of training, zero-shot code generation performance of LLMs can be substantially improved without online sampling. Additionally, offline RL produces performance gains across models ranging from 0.5B to 7B parameters, although the extent of improvement varies among model families.
