agurung/cobalt-seeded-rl-base-ramp25-stoppen-gen4k-ep2-ncp10-bvs8
The agurung/cobalt-seeded-rl-base-ramp25-stoppen-gen4k-ep2-ncp10-bvs8 is a 4 billion parameter OpenRLHF GRPO reinforcement-learning checkpoint for Qwen3-4B, specifically fine-tuned for code generation. This model was seeded from the base Qwen3-4B and optimized using a binary code-correctness reward signal. It excels at generating correct code, having been identified as the best checkpoint by pass@8 in its training run, making it suitable for programming assistance and automated code generation tasks.
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Model Overview
The agurung/cobalt-seeded-rl-base-ramp25-stoppen-gen4k-ep2-ncp10-bvs8 is a 4 billion parameter reinforcement-learning checkpoint based on the Qwen/Qwen3-4B-Instruct-2507 model. Developed by agurung, this model utilizes the OpenRLHF GRPO algorithm and was trained without an SFT seed, applying RL directly to the base Qwen3-4B.
Key Capabilities
- Optimized for Code Correctness: The model's primary strength lies in generating functionally correct code, as evidenced by its "best by pass@8" metric during training.
- Reinforcement Learning: Trained using a GRPO algorithm with a binary code-correctness reward signal, meaning it learns directly from whether its generated code passes tests.
- Anti-Truncation Shaping: Incorporates a "stop-properly penalty" where truncated samples receive a -1.0 reward, and an "overlong penalty" for responses nearing the maximum token limit, encouraging complete and concise outputs.
Good For
- Code Generation: Ideal for tasks requiring the generation of executable and correct code, particularly for problems similar to those in the
cobalt-trainfrontier dataset. - Programming Assistance: Can be used in applications where a model needs to produce high-quality, test-passing code snippets or solutions.
This checkpoint represents a specific stage (global step 48) of an RL training run, focusing on maximizing code pass rates.