Training a 1.5B Parameter Model for OCaml Code Generation with GRPO and RLVR
Key Takeaways
- ▸Small models (1.5B parameters) can be effectively specialized for niche programming languages through GRPO and RLVR fine-tuning without massive compute budgets
- ▸Reward function design is critical to RL-based code generation—graduated rewards tracking compilation progress vastly outperform binary pass/fail signals
- ▸LoRA-based training on single GPUs (48GB VRAM) enables practical fine-tuning while maintaining manageable memory footprints and enabling rapid experimentation
Summary
An engineer shares a detailed technical exploration of training a small 1.5B parameter language model specifically for OCaml code generation using reinforcement learning techniques GRPO (Group Relative Policy Optimization) and RLVR (Reinforcement Learning from Verification Results). The project used Alibaba's Qwen2.5-Coder-1.5B-Instruct as the base model and involved creating a specialized OCaml dataset by translating programming problems from other languages using Claude. The researcher demonstrated how small models can be effectively fine-tuned for domain-specific tasks through careful constraint-setting, reward design, and hyperparameter tuning, while keeping computational costs practical with LoRA adapters and single-GPU training. The work documents how graduated reward functions recognizing compilation stages significantly outperform simple binary metrics, and how local inference on consumer hardware becomes viable for specialized code generation tasks.
- Domain-specific datasets can be created efficiently by translating or porting existing problem sets to the target language using existing AI tools
Editorial Opinion
This is valuable technical documentation for practitioners exploring reinforcement learning for code generation. The author's pragmatic focus on accessible training methods and real-world constraints makes this especially relevant for researchers with limited resources. However, the lack of comparative benchmarks against other training approaches, larger models, or baseline performance limits the broader impact and reproducibility of the findings.
