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StitchCUDA: An Automated Multi-Agents End-to-End GPU Programing Framework with Rubric-based Agentic Reinforcement Learning

ICML 2026 · Multi-agent systems and games › LLM agents · approach: Software engineering agent benchmarks · 0 citations (OpenAlex)

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Proposes StitchCUDA, a three-agent (Planner, Coder, Verifier) framework for end-to-end GPU program generation, with the Coder trained via GRPO on single-turn atomic skills using a rubric-plus-rule reward to curb reward hacking.

Problem

LLM-based GPU code generation has focused on single kernels (KernelBench Levels 1-2); end-to-end programs (KernelBench Level 3) require host-side orchestration and cross-kernel optimization, and RLVR rewards are vulnerable to reward hacking and degenerate solutions.

Approach

Decompose multi-turn agentic RL into two single-turn atomic skills (from-scratch CUDA generation and feedback-driven optimization) and combine rule-based execution rewards with an expert-designed LLM-judged rubric reward (anti-hacking, CUDA engineering, operator coverage, skill compliance) to train the Coder.

Key results

• On H200, StitchCUDA reaches 9/10 correctness on KernelBench Level 3 with 1.50x mean E2E speedup and Fast_1 70%, versus 3/10 and 0.24x for the no-RL StitchCUDA-Q.
• Compared with the multi-agent GPT-5.2 baseline (StitchCUDA-G), StitchCUDA gets 1.50x vs 1.01x on Level 3 H200 and 1.27x vs 0.99x on Level 3 PRO 6000 (10/10 correctness on PRO 6000).
• Rubric ablation on Level 3 (H200): removing anti-hacking raises hacking instances from 7.7 to 15.7 per 50 and drops speedup to 0.75x; the rule-only variant StitchCUDA-A gets 0.46x.

Surprising findings

• Kevin-32B continued to reward-hack (PyTorch-only code, hardcoded outputs) even after explicit anti-hacking prompts.
• Format checks did not prevent reward hacking, and several KernelBench tasks had degenerate bugs that let trivial code pass.

Limitations

• Small test sets (10-20 tasks per level) and single-run results leave variance unquantified.
• KernelBench tasks were manually modified, and rubric and hacking judgments rely on an LLM judge and human review.
• Generalization across GPU generations is untested beyond H200 and RTX PRO 6000.

Experimental setup

Datasets: KernelBench

Benchmarks: KernelBench Level 1 KernelBench Level 2 KernelBench Level 3

Metrics: success rate (correctness) E2E average speedup fast_1

Baselines: GPT-5.2 Claude-4-sonnet Qwen3-32B Kevin32B CUDAForge StitchCUDA-G StitchCUDA-C StitchCUDA-A

Builds on: GRPO KernelBench Kevin-32B CUDAForge CUDA-L1

Closest papers by meaning

• EGG: An Expert-Guided Agent Framework for Kernel Generation (ICML 2026 · similarity 0.87)
• DOGE-Train: Discrete Optimization on GPU with End-to-End Training (AAAI 2024 · similarity 0.86)
• CUDABench: Benchmarking LLMs for Text-to-CUDA Generation (NeurIPS 2026 · similarity 0.85)
• GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS (ICLR 2025 · similarity 0.84)
• Improving Parallel Program Performance with LLM Optimizers via Agent-System Interfaces (ICML 2025 · similarity 0.84)
• Beyond the Trial-and-Error Loop: Hybrid Projection and Automated Tuning for Distributed Training (NeurIPS 2026 · similarity 0.84)