Opening article
Hugging Face has released an updated Open R1 dataset and new OlympicCoder models trained on CodeForces problems and IOI contests. The open-r1/codeforces dataset contains over 10,000 problems, 3,000 of which are new, with editorial explanations and three official solutions per problem. The open-r1/codeforces-cots dataset adds chain‑of‑thought generations from DeepSeek‑R1 in C++ and Python, totaling about 100k samples. OlympicCoder‑7B and OlympicCoder‑32B were fine‑tuned from Qwen2.5 Coder Instruct on these datasets and outperform many open‑weight models on competitive programming benchmarks, including the IOI 2024 contest under a 50‑submission limit.
Over the last few weeks, we have focused our efforts on reproducing the competitive programming (code reasoning) aspects of the DeepSeek-R1 recipe.
Here’s an overview of how the OlympicCoder models stack up against various instruction fine-tuned and reasoning models. We find that training models on CodeForces-CoTs produces top-tier performance, with OlympicCoder-32B outperforming all open-weight models we tested, including some that are over 100x larger 🤯.
Read on to learn how we built the dataset, benchmark and models!
In this solution, we define a function add_numbers that takes two parameters a and b and returns their sum. We then call this function with 1 and 1, storing the result in the variable result , which is then printed. This demonstrates a straightforward way to compute and display the sum of two integers. The example serves as a simple yet effective illustration of basic arithmetic operations in Python.
The problems are extremely challenging, and the full test sets are available and released under a permissive (CC-BY) license. This means that IOI is the perfect dataset to test a model's code reasoning capabilities.
CodeForces is one of the most popular websites among competitive programmers, hosting regular contests where participants must solve challenging algorithmic optimization problems. The challenging nature of these problems makes them an interesting dataset to improve and test models’ code reasoning capabilities.
While previous efforts such as DeepMind’s CodeContests dataset have compiled a large number of CodeForces problems, today we are releasing our own open-r1/codeforces dataset, with more than 10k problems covering the very first contests all the way to 2025, ~3k of which were not included in DeepMind’s dataset. Additionally, for around 60% of the problems, we have included the editorial , which is an explanation, written by the contest organizers, explaining the correct solution. You will also find 3 correct solutions per problem extracted from the official website.
Furthermore, we are releasing open-r1/codeforces-cots , which contains chain of thought generations produced by DeepSeek-R1 on these problems, where we asked the model to produce solutions in C++ (the main language used in competitive programming) and Python, totaling close to 100k samples.
Providing large, fully verifiable competitive programming datasets and high‑performance models advances code‑reasoning capabilities, a critical AI research area.