Best practices for reinforcement learning policy optimization. Use when working on RL agents, PPO, SAC, or reward design.
Best practices for designing reproducible ML experiments. Use when planning ablations, baselines, or controlled experiments.
Best practices for object detection tasks. Use when working on COCO, VOC, or detection architectures like YOLO and DETR.
Best practices for language model pretraining and fine-tuning. Use when generating or reviewing NLP training code.
Best practices for image classification tasks. Use when working on CIFAR, ImageNet, or other classification benchmarks.
Best practices for building robust PyTorch training loops. Use when generating or reviewing ML training code.
Best practices for LLM alignment techniques including RLHF, DPO, and instruction tuning. Use when working on alignment or safety.
Use AutoResearchClaw when an agent should turn a raw research topic into literature review, experiment planning, draft writing, and verification artifacts instead of improvising…