ReaComp: Compiling LLM Reasoning into Symbolic Solvers for Efficient Program Synthesis
LLMs can solve program synthesis tasks but remain inefficient and unreliable on hard instances requiring large combinatorial search. Given a small set of reasoning traces, we use coding agents to compile them into reusable symbolic program synthesizers over constrained DSLs. The resulting solvers require no LLM calls at test time and are strong standalone systems: symbolic solver ensembles reach 91.3% accuracy on PBEBench-Lite and 84.7% on PBEBench-Hard, outperforming LLMs with test-time scaling for the latter by +16.3 percentage points at zero LLM inference cost. They also complement LLM search, improving PBEBench-Hard accuracy from 68.4% to 85.8% while reducing reported token usage by 7...
| Authors | Atharva Naik, Yash Mathur, Prakam et al. |
|---|---|
| Primary category | cs.CL |
| Published | 6 May 2026 |
| Last updated at source | 6 May 2026 |
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