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3,641 results · Page 17 of 102

simpleRL-reason

AI developer platforms

simpleRL-reason is an open-source reinforcement learning training codebase for enhancing reasoning ability, released on GitHub by the NLP team at the Hong Kong University of Science and Technology. It provides a simple reinforcement learning training scheme that can improve the reasoning ability of base models using only rule-based rewards and the GSM8K/Math datasets. The project has successfully conducted zero RL training on 10 different base models, including Llama3 8B, Mistral 7B/24B, DeepSeekMath 7B, Qwen2.5 0.5B/1.5B/7B/14B/32B, and Qwen2.5-Math-7B. Using limited data of 8K examples, the accuracy improvement reaches 10 to more than 20 absolute percentage points. The project has open-sourced the code, model checkpoints, and analysis tools, and provides related papers and blogs. The code is implemented based on Verl, supports custom environment settings and FSDP training, uses the GRPO algorithm, and accelerates training with Ray and vLLM.

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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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