Execution-Grounded Credit Assignment for GRPO in Code Generation
Critic-free reinforcement learning with verifiable rewards (RLVR) improves code generation by optimizing unit-test pass rates, but GRPO-style updates suffer from coarse credit assignment: a single outcome signal is spread uniformly across long programs even when failure stems from a localized semantic error. We propose Execution-Grounded Credit Assignment (EGCA), which localizes GRPO updates using execution traces. For programs that satisfy algorithmic constraints but fail tests, EGCA executes the candidate and a canonical reference solution (curated once offline; used for analysis, not supervision) under identical instrumentation, identifies the earliest semantic divergence, and assigns ...
| Authors | Abhijit Kumar, Natalya Kumar, Shikhar Gupta |
|---|---|
| Primary category | cs.LG |
| Published | 17 March 2026 |
| Last updated at source | 17 March 2026 |
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