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arxiv:2409.19715

Coffee-Gym: An Environment for Evaluating and Improving Natural Language Feedback on Erroneous Code

Published on Sep 29
· Submitted by hyungjoochae on Oct 1
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Abstract

This paper presents Coffee-Gym, a comprehensive RL environment for training models that provide feedback on code editing. Coffee-Gym includes two major components: (1) Coffee, a dataset containing humans' code edit traces for coding questions and machine-written feedback for editing erroneous code; (2) CoffeeEval, a reward function that faithfully reflects the helpfulness of feedback by assessing the performance of the revised code in unit tests. With them, Coffee-Gym addresses the unavailability of high-quality datasets for training feedback models with RL, and provides more accurate rewards than the SOTA reward model (i.e., GPT-4). By applying Coffee-Gym, we elicit feedback models that outperform baselines in enhancing open-source code LLMs' code editing, making them comparable with closed-source LLMs. We make the dataset and the model checkpoint publicly available.

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Hi @hyungjoochae congrats on this work!

Regarding "We make the dataset and the model checkpoint publicly available." => are you planning to share these on the hub? Would be great to then link them to this paper, enabling people to discover them :) see https://huggingface.co/docs/hub/en/paper-pages#linking-a-paper-to-a-model-dataset-or-space.

Let us know if you need any help!

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