Paper
Execution-Grounded Credit Assignment for GRPO in Code Generation
Authors
Abhijit Kumar, Natalya Kumar, Shikhar Gupta
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.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.16158v1</id>\n <title>Execution-Grounded Credit Assignment for GRPO in Code Generation</title>\n <updated>2026-03-17T06:22:44Z</updated>\n <link href='https://arxiv.org/abs/2603.16158v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.16158v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>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.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.LG'/>\n <published>2026-03-17T06:22:44Z</published>\n <arxiv:comment>Accepted at SPOT ICLR 2026 (https://openreview.net/forum?id=nqkVB5EVXJ)</arxiv:comment>\n <arxiv:primary_category term='cs.LG'/>\n <author>\n <name>Abhijit Kumar</name>\n </author>\n <author>\n <name>Natalya Kumar</name>\n </author>\n <author>\n <name>Shikhar Gupta</name>\n </author>\n </entry>"
}