Open R1: Update #2
Hugging Face's Open R1 project releases its second progress update on the open-source replication of DeepSeek-R1's reasoning capabilities. The update likely covers training progress, dataset releases, and intermediate model checkpoints as the team works toward a fully open reproduction of the reasoning model pipeline. Open R1 is a community-driven effort to make the techniques behind frontier reasoning models accessible to researchers.
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Open R1: Update #3
Hugging Face's Open R1 project releases its third update, continuing the open-source replication effort of DeepSeek-R1's reasoning model training pipeline. The update likely covers progress on data, training runs, and evaluation results for the community-driven reproduction. This is part of an ongoing effort to make frontier reasoning model capabilities accessible via open weights and open training code.
Open R1: Update #4
Hugging Face's Open R1 project releases its fourth progress update on the open reproduction of DeepSeek-R1. The update likely covers training progress, dataset releases, and evaluation results for the open-weights reasoning model effort. This project is a community-driven attempt to replicate and open-source the techniques behind DeepSeek-R1's chain-of-thought reasoning capabilities.
Open-R1: Update #1 — Open Reproduction of DeepSeek-R1
Hugging Face's Open-R1 project provides a first progress update on its open reproduction of DeepSeek-R1, a reasoning-focused language model. The update covers early training runs, dataset construction, and evaluation results aimed at replicating DeepSeek-R1's chain-of-thought reasoning capabilities. This effort is part of the broader open-weights community push to reproduce frontier reasoning models transparently.
Open-R1: a fully open reproduction of DeepSeek-R1
Hugging Face announced Open-R1, a community effort to fully reproduce DeepSeek-R1's training pipeline using open-source components. The project aims to replicate the data, training, and evaluation stages of DeepSeek-R1, making the entire process transparent and accessible. This follows significant interest in DeepSeek-R1's reinforcement-learning-based reasoning approach and addresses the lack of fully open reproduction of that methodology.
Hugging Face open reproduction of DeepSeek-R1
Hugging Face has published an open reproduction of DeepSeek-R1, the reasoning-focused language model, on GitHub. The project aims to replicate DeepSeek-R1's training methodology and capabilities in an open-weights setting. This contributes to the broader effort to make frontier reasoning model techniques accessible to the research community.
DeepSeek-R1 Release: Open-Source Reasoning Model on Par with OpenAI o1
DeepSeek has released DeepSeek-R1, a reasoning-focused large language model claiming performance parity with OpenAI o1 on math, code, and reasoning benchmarks. The model is fully open-source under the MIT License, including weights and outputs, enabling distillation and commercial use. Six distilled smaller models (up to 32B and 70B) are also released, with the 32B and 70B variants reportedly matching OpenAI o1-mini. API access is live at significantly lower pricing than comparable frontier models ($0.55/M input tokens, $2.19/M output tokens).
Mini-R1: Reproducing DeepSeek R1 'Aha Moment' — An RL Tutorial
A Hugging Face blog post demonstrates how to reproduce DeepSeek R1's emergent 'aha moment' reasoning behavior using reinforcement learning on a countdown game task. The tutorial walks through training a smaller model with RL to exhibit chain-of-thought self-correction, similar to the behavior observed in DeepSeek R1. This serves as a practical open-source replication effort aimed at demystifying R1's training dynamics.
Open-source DeepResearch – Freeing our search agents
Hugging Face published a blog post introducing Open Deep Research, an open-source replication of agentic deep research capabilities (similar to OpenAI's Deep Research). The project aims to build open-weight search agents capable of multi-step web research and synthesis. The post details the architecture, tooling, and early benchmark results of the system.