Can an LLM act as a selective model of a GPU during evolutionary search, by reasoning + forecasting a kernel’s runtime but deferring to a GPU when unsure? We produced 12k kernels + runtimes from evolutionary search, costing 400M reasoning tokens + 600 GPU-hours to answer this. In our work GPU Forecasters, we study language models as selective surrogates for GPU kernel optimization. 1️⃣ Off-the-shelf LLMs can forecast how a GPU responds to a candidate kernel with non-trivial accuracy. If we rank candidates by these predictions and measure only the top 10% on a GPU, the fastest kernel we find is within 20% of the best in the pool. 2️⃣ We want LLMs to not just be accurate but also calibrated, so that we can use their uncertainty for selective prediction: during search, we should trust only confident forecasts and verify less confident forecasts by sending them to the GPU. 3️⃣ We train an open-weights surrogate (GPT-OSS-20B) with RL to improve both accuracy and calibration. Calibration-shaped rewards improve both confidence reliability and ranking ability, while correctness rewards alone do not. 4️⃣ Inside a real kernel search, the surrogate finds faster kernels than an equal-GPU-budget baseline by considering more candidates per measurement. 5️⃣ We release 12,388 LLM-generated GPU kernels with measured runtimes spanning 118 operations, CUDA and Triton backends, 3 GPU types, taking 400M tokens + 600 GPU-hours to produce. This dataset can be used for analyzing LLM-driven evolutionary program search dynamics, post-training LLMs for kernel code generation, and things we didn’t get a chance to explore, like training reward models! Thread 🧵👇

In our newest work on environment generation, we discover environment features (as executable functions) that explain why an embodied navigation agent fails, and use those features to generate novel learnable environments where the policy gets practice with the conditions that make it fail. Over multiple rounds, we can discover new weaknesses and keep generating new environments! 🧵👇