OpenCodeReasoning: Advancing Data Distillation for Competitive Coding
Since the advent of reasoning-based large language models, many have found
great success from distilling reasoning capabilities into student models. Such
techniques have significantly bridged the gap between reasoning and standard
LLMs on coding tasks. Despite this, much of the progress on distilling
reasoning models remains locked behind proprietary datasets or lacks details on
data curation, filtering and subsequent training. To address this, we construct
a superior supervised fine-tuning (SFT) dataset that we use to achieve
state-of-the-art coding capability results in models of various sizes. Our
distilled models use only SFT to achieve 61.8% on LiveCodeBench and 24.6% on
CodeContests, surpassing alternatives trained with reinforcement learning. We
then perform analysis on the data sources used to construct our dataset, the
impact of code execution filtering, and the importance of instruction/solution
diversity. We observe that execution filtering negatively affected benchmark
accuracy, leading us to prioritize instruction diversity over solution
correctness. Finally, we also analyze the token efficiency and reasoning
patterns utilized by these models. We will open-source these datasets and
distilled models to the community.
67edf81f5e87fcaa485b0df5
Wasi Uddin Ahmad
claimed_verified
2025-04-04T07:09:48.768Z
67ec45ba92673a326c067b52
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/PcD60kFRczOblxgc1bWcl.png
Wasi Uddin Ahmad
user
wasiuddina
67edf81f5e87fcaa485b0df6
Sean Narenthiran
67edf81f5e87fcaa485b0df7
Somshubra Majumdar
claimed_verified
2025-04-03T07:52:08.410Z
61eb5b2f55d896e8b6c1c376
https://cdn-avatars.huggingface.co/v1/production/uploads/1652937892550-61eb5b2f55d896e8b6c1c376.jpeg
Somshubra Majumdar
user
smajumdar
67edf81f5e87fcaa485b0df8
Aleksander Ficek
67edf81f5e87fcaa485b0df9
Siddhartha Jain
67edf81f5e87fcaa485b0dfa
Jocelyn Huang
67edf81f5e87fcaa485b0dfb
Vahid Noroozi
67edf81f5e87fcaa485b0dfc
Boris Ginsburg
OpenCodeReasoning: Advancing Data Distillation for Competitive Coding
Since the advent of reasoning-based large language models, many have found
great success from distilling reasoning capabilities into student models. Such
techniques have significantly bridged the gap between reasoning and standard
LLMs on coding tasks. Despite this, much of the progress on distilling
reasoning models remains locked behind proprietary datasets or lacks details on
data curation, filtering and subsequent training. To address this, we construct
a superior supervised fine-tuning (SFT) dataset that we use to achieve
state-of-the-art coding capability results in models of various sizes. Our
distilled models use only SFT to achieve 61.8% on LiveCodeBench and 24.6% on
CodeContests, surpassing alternatives trained with reinforcement learning. We
then perform analysis on the data sources used to construct our dataset, the
impact of code execution filtering, and the importance of instruction/solution
diversity. We observe that execution filtering negatively affected benchmark
accuracy, leading us to prioritize instruction diversity over solution
correctness. Finally, we also analyze the token efficiency and reasoning
patterns utilized by these models. We will open-source these datasets and
distilled models to the community.
A superior SFT dataset improves coding capabilities in distilled reasoning models surpassing reinforcement learning alternatives.
supervised fine-tuning LiveCodeBench CodeContests reinforcement learning code execution filtering instruction diversity solution correctness token efficiency reasoning patterns