thinking

We built a self-healing coding agent that searches over compact DSL implementation plans before generating code. The LLM proposes semantic reasoning branches, while a Rainbow RL controller learns which branch to select, refine, compile, backtrack from, or terminate using verifier feedback from compile and tests. This decision can be passed back to the LLM to converge to a final solution by pruning the search space. On our held-out synthetic benchmark, Rainbow with Gemma E2B (2 billion parameters) matches Codex 5.4 Low’s 80% solve rate while using roughly 140x fewer tokens and running about 4x faster. Developed and evaluated entirely on a single M3 MacBook Pro, this is an existence proof that a modular learned reasoning layer can help small local models approach frontier-agent performance at a fraction of the cost. The key idea is not to replace the LLM, but to spend expensive LLM budget only on branches that are likely to work, turning code repair from one-shot generation into a learned, verifier-guided search process.