Execution-Grounded Credit Assignment for GRPO in Code Generation

• URL: http://arxiv.org/abs/2603.16158v1
• Date: Tue, 17 Mar 2026 06:22:44 GMT
• Title: Execution-Grounded Credit Assignment for GRPO in Code Generation
• Abstract summary: Execution-Grounded Credit Assignment (EGCA) localizes GRPO updates using execution traces.<n>For programs that satisfy algorithmic constraints but fail tests, EGCA executes the candidate and a canonical reference solution.<n>EGCA is a drop-in modification requiring no critic, auxiliary loss, or learned verifier.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 advantage only to the corresponding token span while masking downstream tokens. EGCA is a drop-in modification requiring no critic, auxiliary loss, or learned verifier, yielding 82.1% pass@1 on HumanEval (+3.1 over GRPO) and 68.9% on MBPP (+1.5) with 18% wall-clock overhead.

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