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3,631 results · Page 34 of 101

Skywork-OR1

AI developer platforms

Skywork-OR1 (Open Reasoner 1) is a series of math and code reasoning models and accompanying training solutions open-sourced by SkyworkAI. This series uses large-scale rule-based reinforcement learning, combined with carefully designed datasets and training recipes, and includes two general reasoning models, Skywork-OR1-7B and Skywork-OR1-32B. According to official materials, Skywork-OR1-32B outperforms Deepseek-R1 and Qwen3-32B on math tasks (AIME24 and AIME25) and performs comparably on code tasks (LiveCodeBench); Skywork-OR1-7B is competitive in math and coding scenarios among models of the same scale. The project has open-sourced model weights, training data, and code, and provides training and evaluation scripts, supports reproduction via Docker and Conda environments, and supports multi-node training using Ray. The official team also released a technical report (arXiv) and Notion Blog, sharing detailed training recipes, experimental results, and analysis. The project also provides the Skywork-OR1-RL-Data training dataset, as well as evaluation scripts for AIME24/AIME25 and LiveCodeBench. Evaluation uses Avg@K as the primary metric rather than Pass@1, to reduce the impact of randomness.

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slime

AI developer platforms

slime is an LLM post-training framework developed by THUDM, designed for scaled reinforcement learning (RL) training. By connecting Megatron with SGLang, it provides two core capabilities: high-performance training and flexible data generation, and unifies training, rollout, data buffering, reward computation, verifier feedback, and environment interaction into a single path. The framework has been used for the release training of models such as GLM-5.3-Flash, GLM-5.3, GLM-5.2, GLM-5.1, GLM-5, GLM-4.7, GLM-4.6, and GLM-4.5, while also supporting model families such as Qwen, DeepSeek, and Llama 3. The design goal of slime is to let training and data generation reinforce each other, without having to cobble together a heavy tech stack composed of fragmented trainers, rollout services, and agent frameworks. As a result, it is small in size, easy to understand and extend, and yet validated through a complete training loop.

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Slow_Thinking_with_LLMs

AI developer platforms

STILL (Slow Thinking with LLMs) is a series of technical research projects on slow-thinking reasoning systems for large language models conducted by the RUCAIBox team at Renmin University of China, with long-term exploration of o1-like reasoning systems. The project has open-sourced a large amount of code, models, training data, and evaluation benchmarks, covering directions such as reinforcement learning exploration mechanisms, tool-augmented reasoning, code-integrated reasoning, autonomous web search, and hallucination mitigation, and has released STILL-1, STILL-2, STILL-3, and multiple derivative models and benchmarks.

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smol-vision

AI developer platforms

smol-vision is an open-source collection of recipes for vision and multimodal AI models, with the goal of shrinking, optimizing, and customizing frontier models. The repository provides several runnable examples, including fine-tuning Kosmos2.5 on OCR and bounding box tasks, fine-tuning Florence-2 on document question answering tasks, and fine-tuning DINOv3 on image classification tasks. The repository is maintained by merveenoyan and is published as a GitHub repository. Because GitHub has been unable to render notebooks properly for a long time, notebooks containing rich outputs have been moved to another location, but the repository is still being continuously updated. The project's specific dependencies, installation commands, license agreement, and ways to participate are not yet provided.

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