# ByteDance Seed and Tsinghua AIR Introduces CUDA Agent: A Large-Scale Agentic RL System for CUDA Kernel Generation > Source: > Published: 2026-08-18 01:10:28+00:00 ByteDance Seed and Tsinghua AIR have released CUDA Agent, an agentic reinforcement learning system that trains a large language model to write GPU kernels that beat a compiler. The gap it targets is narrow but stubborn: frontier models already produce *correct* CUDA, they just produce slow CUDA. On KernelBench, the base model Seed1.6 passes 74.0% of tasks yet outruns `torch.compile` on only 27.2% of them, at a 0.69× geometric-mean speedup which means its kernels are, on average, slower than what the compiler generates on its own. CUDA Agent closes that gap by putting the model inside a real CUDA development environment with profiling, correctness checks and a permission-locked sandbox, then training it with PPO for 150 steps at a 131,072-token context. The result is a 98.8% pass rate and a 96.8% faster-than-`torch.compile` rate across the 250-task benchmark, at 2.11× geomean over compile — roughly 40 points ahead of Claude Opus 4.5 and Gemini 3 Pro on the hardest Level-3 split. **Is it deployable?** Partly, but the trained agent is not released. It is built on [Seed1.6](https://seed.bytedance.com/en/seed1_6), a proprietary MoE model with 23B active and 230B total parameters, and the paper ships no weights. Public: the [CUDA-Agent-Ops-6K](https://huggingface.co/datasets/BytedTsinghua-SIA/CUDA-Agent-Ops-6K) dataset, the `SKILL.md` spec and the reward and warm-up recipes. **Which companies**: The profiling sandbox alone used 128 NVIDIA H20 GPUs, which puts full replication inside frontier labs, GPU clouds and large infrastructure teams. Mid-size teams can still adopt the parts — dataset, milestone reward, anti-reward-hacking constraints, skill spec — on top of an open base model. **Industries and applications**: AI infrastructure and inference serving, GPU cloud, autonomous driving, quantitative trading, medical imaging and recommendation systems — anywhere fused kernels sit on a latency-critical path. Uses include fusing operator sequences `torch.compile` handles poorly, cutting cost per token, and re-tuning kernels across GPU generations. **Data synthesis** The research team crawls reference operators from the `torch` and `transformers` libraries. An LLM then samples up to five `torch` operator classes and stacks them into one fused layer. A filter keeps only operators that execute in both eager and compile modes, are deterministic, produce non-constant outputs, and run between 1 ms and 100 ms in eager mode. Samples with AST similarity above 0.9 to any KernelBench task are removed. The result is CUDA-Agent-Ops-6K: 6,000 samples, 83.77% of them two-operator compositions. **Environment and reward** The agent loop mirrors [OpenHands](https://arxiv.org/abs/2407.16741) tooling — Bash, Read/Write, Edit/MultiEdit, Glob, Grep, NotebookEdit, BashOutput, KillBash — under a ReAct pattern. CUDA instructions ship in the [Agent Skills](https://www.anthropic.com/engineering/equipping-agents-for-the-real-world-with-agent-skills) format. `SKILL.md` tells the model to profile the PyTorch model, rewrite `model_new.py` with custom kernels, compile in a GPU sandbox, and iterate until the kernel is at least 5% faster than `torch.compile` at `atol=1e-2, rtol=1e-2` . Reward hacking gets five countermeasures: permission-locked verification and profiling scripts, context managers that forbid `torch.nn.functional` fallbacks, checks against five random inputs, profiling with device synchronization and warm-up, and no web search tool. The reward is discrete rather than a raw speedup ratio. r ∈ {−1, 1, 2, 3}: −1 on correctness failure, 3 if the kernel clears both eager and `torch.compile` by more than 5%, 2 if it clears eager only, 1 otherwise. **Results** Table 1, overall: 98.8% pass rate, 98.4% faster than eager, 96.8% faster than `torch.compile` , at 2.60× and 2.11× geomean respectively. Level 2 (operator sequences) is the strongest split: 100% pass, 100% faster rate, 2.80× over `torch.compile` . Level 3 lands at 94.0% pass, 90.0% faster rate and 1.52×, roughly 40 points above Claude Opus 4.5 (50.0%) and Gemini 3 Pro (52.0%) on faster rate versus compile. One inconsistency: the abstract and introduction state 100% / 100% / 92% faster rates for Levels 1–3, while Table 1 reports 97.0% / 100.0% / 90.0%. Table 1 is the main results table. Ablations are blunt. Removing the agent loop drops faster rate versus compile from 96.8% to 14.1%. A raw speedup reward gives 60.4%, no RFT gives 49.8% plus reward collapse, no value pretraining gives 50.9% plus runaway trajectories. Case studies show what the policy learns. A diagonal matmul rewritten as row-wise scaling: 73.31× over `torch.compile` . A matmul-divide-sum-scale chain reordered and fused: 24.04×. A ResNet BasicBlock with BatchNorm folded into convolution and`cudnnConvolutionBiasActivationForward` : 3.59×. **Key Takeaways** - CUDA Agent hits 98.8% pass rate and 96.8% faster-than- `torch.compile` rate on KernelBench, at 2.11× geomean. - Level 2 fusion is the standout: 100% faster rate and 2.80× over `torch.compile` . - The discrete milestone reward beats a raw speedup ratio by 36.4 points on faster rate. - RFT plus value pretraining is what turns a 17-step collapse into 150 stable steps. - Weights are closed; the 6,000-sample dataset, `SKILL.md` and the recipe are public. Check out the [ Paper](https://arxiv.org/abs/2602.24286), the [and the](https://cuda-agent.github.io/) **Project Page**[.](https://huggingface.co/datasets/BytedTsinghua-SIA/CUDA-Agent-Ops-6K) **Dataset****Also, feel free to follow us on** **and don’t forget to join our**[Twitter](https://x.com/intent/follow?screen_name=marktechpost) **and Subscribe to** [150k+ML SubReddit](https://www.reddit.com/r/machinelearningnews/)**. Wait! are you on telegram?** [our Newsletter](https://magic.beehiiv.com/v1/f5e63dd4-5653-4f09-83e2-321a8b1ba526?email={{email}}) [now you can join us on telegram as well.](https://t.me/machinelearningresearchnews)Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? [Connect with us](https://forms.gle/wbash1wF6efRj8G58) Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. 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