agurung/cobalt-seeded-rl-base-ramp25-stoppen-gen4k-ep2-ncp10-mgroot16
The agurung/cobalt-seeded-rl-base-ramp25-stoppen-gen4k-ep2-ncp10-mgroot16 is a 4 billion parameter Qwen3-based model, fine-tuned using OpenRLHF GRPO. This checkpoint is specifically optimized for code generation tasks, leveraging a binary code-correctness reward signal. It excels at solving programming problems that the base model struggled with, making it suitable for applications requiring robust code output.
Loading preview...
Model Overview
The agurung/cobalt-seeded-rl-base-ramp25-stoppen-gen4k-ep2-ncp10-mgroot16 is a specialized reinforcement learning (RL) checkpoint based on the Qwen3-4B architecture. Developed by agurung, this model was fine-tuned using the OpenRLHF GRPO algorithm, directly applied to the base Qwen3-4B model without an initial Supervised Fine-Tuning (SFT) seed. It represents the best performing checkpoint by pass@8 in its training run.
Key Capabilities and Training
- Code Generation Focus: The model is specifically trained and validated on the
cobalt-traindataset, targeting programming problems that the base model could only solve on a limited number of samples. - Reward Signal: Training utilizes a binary code-correctness reward, meaning the model is rewarded 1.0 if its generated program passes problem tests and 0.0 otherwise, directly optimizing for functional code.
- RL Algorithm: Employs GRPO (group-normalized advantages, no KL penalty) with specific anti-truncation and overlong response penalties to guide generation.
- Context Length: Supports a maximum of 4096 new tokens per rollout during training, indicating its capacity for generating substantial code blocks.
Use Cases
This model is particularly well-suited for:
- Automated Code Generation: Generating solutions for programming challenges or specific coding tasks.
- Code Completion and Repair: Assisting developers by producing correct and functional code snippets.
- Educational Tools: Creating systems that can solve or demonstrate solutions to coding problems.