Papers › OpenChat: Advancing Open-source Language Models with Mixed-Quality Data

OpenChat: Advancing Open-source Language Models with Mixed-Quality Data

20 Sep 2023arXiv:2309.11235archive 2025-07-28

Guan Wang, Sijie Cheng, Xianyuan Zhan, Xiangang Li, Sen Song, Yang Liu

Nowadays, open-source large language models like LLaMA have emerged. Recent developments have incorporated supervised fine-tuning (SFT) and reinforcement learning fine-tuning (RLFT) to align these models with human goals. However, SFT methods treat all training data with mixed quality equally, while RLFT methods require high-quality pairwise or ranking-based preference data. In this study, we present a novel framework, named OpenChat, to advance open-source language models with mixed-quality data. Specifically, we consider the general SFT training data, consisting of a small amount of expert data mixed with a large proportion of sub-optimal data, without any preference labels. We propose the C(onditioned)-RLFT, which regards different data sources as coarse-grained reward labels and learns a class-conditioned policy to leverage complementary data quality information. Interestingly, the optimal policy in C-RLFT can be easily solved through single-stage, RL-free supervised learning, which is lightweight and avoids costly human preference labeling. Through extensive experiments on three standard benchmarks, our openchat-13b fine-tuned with C-RLFT achieves the highest average performance among all 13b open-source language models. Moreover, we use AGIEval to validate the model generalization performance, in which only openchat-13b surpasses the base model. Finally, we conduct a series of analyses to shed light on the effectiveness and robustness of OpenChat. Our code, data, and models are publicly available at https://github.com/imoneoi/openchat and https://huggingface.co/openchat.

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Tasks

Arithmetic ReasoningCode GenerationMath Word Problem Solving

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Arithmetic Reasoning GSM8K OpenChat-3.5 7B Accuracy 77.3 #81 of 164 Archive leaderboard report
Arithmetic Reasoning GSM8K OpenChat-3.5 7B Parameters (Billion) 7 #81 of 164 Archive leaderboard report
Math Word Problem Solving MATH OpenChat-3.5-1210 7B Accuracy 28.9 #91 of 135 Archive leaderboard report
Math Word Problem Solving MATH OpenChat-3.5-1210 7B Parameters (Billions) 7 #91 of 135 Archive leaderboard report
Math Word Problem Solving MATH OpenChat-3.5 7B Accuracy 28.6 #92 of 135 Archive leaderboard report
Math Word Problem Solving MATH OpenChat-3.5 7B Parameters (Billions) 7 #92 of 135 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

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