Training a small model to write better OCaml with RLVR and GRPO (blog.nilenso.com)

In a recent exploration, a small 1.5B language model was trained using Reinforcement Learning with Value Representation (RLVR) and Gradient-based Reinforcement Learning with Policy Optimization (GRPO) to enhance its capability for generating OCaml code. The training utilized varied sources from public GitHub repositories and incorporated a newly designed reward system that provided nuanced feedback based on compilation and testing phases, rather than binary pass/fail criteria. This approach significantly improved the model's ability to produce compilable and valid OCaml code, achieving a higher rate of success in generating executable solutions. The experiment is notable for the AI/ML community as it highlights the effectiveness of using small models for specialized tasks, showcasing that even without extensive resources, meaningful advancements can be made through innovative training methods. The results showed a shift towards a more functional programming style, evident in the model's preference for pattern matching and recursive helper functions over imperative constructs. While the trained model did show regressions in some specific areas, the overall improvements indicate valuable insights into the training dynamics of smaller models and the careful tuning of reward mechanisms, making this a significant contribution to ongoing discussions on model efficiency and adaptability in AI code generation tasks.