Skill UI

This skill manages the entire lifecycle of long-running deep learning training and inference jobs deployed on remote, ephemeral computing environments (e.g., AutoDL, RunPod, bare SSH, Slurm, K8s). It provides critical capabilities like resilient checkpointing, managing spot instance preemption, debugging resource failures (OOM, NaN), and ensuring safe billing teardown, making it essential for professional ML Ops on rented resources.

This skill provides expert capabilities for training and fine-tuning transformer language models using the TRL (Transformers Reinforcement Learning) library. It supports state-of-the-art post-training methods, including Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), Group Relative Policy Optimization (GRPO), and Reward Model training. Use this skill to align and customize foundation models for advanced tasks via CLI commands.

This tool manages the lifecycle of learned intelligence patterns, implementing the crucial DISTILL and CONSOLIDATE phases of the 4-step intelligence pipeline. It enables the capture of successful task knowledge, supports multi-domain adaptation via SONA or MicroLoRA, and consolidates learned patterns into long-term storage to prevent catastrophic forgetting.

This guide helps authors position their machine learning systems manuscripts for the rigorous Conference on Machine Learning and Systems (MLSys). It details the venue's specific focus on system-level contributions, including training/inference infrastructure, compilers, data systems, and hardware-aware ML. Learn how to frame your work to appeal to systems reviewers by emphasizing measurable impacts on capability, cost, reliability, or deployment scale.

This comprehensive guide outlines best practices for packaging and releasing scientific artifacts—including code, training configurations, demonstration data, and policy checkpoints—for robotics research papers. It details how to prepare materials for anonymous reviewer assessment and for durable public release, ensuring the work is fully reproducible by the academic community.

A comprehensive guideline for researchers developing robot-learning papers. It details best practices for experimental design, emphasizing statistical rigor, handling stochasticity (training seeds, initial states), bridging the sim-to-real gap, and ensuring fairness across baseline policy families (BC/RL/VLA). This resource helps ensure claims are supported by credible evidence.

This comprehensive guide establishes a rigorous framework for ensuring reproducibility in robot learning research. It details how to document both the "rerunnable half" (software, simulation versions, training configs, seeds) and the "auditable half" (physical hardware specs, sensors, firmware). Adhering to these standards allows experts to verify and rebuild the experimental results.

A comprehensive guide for hardening deep learning papers, particularly in Computer Vision (CVPR). It details best practices for achieving rigorous reproducibility by utilizing the Compute Reporting Form (CRF), maintaining a detailed recipe ledger, ensuring benchmark split hygiene, and meticulously documenting all training hyperparameters, seeds, and variance analyses for academic publication.

A comprehensive guide for authors submitting computer vision papers (e.g., to ECCV). It outlines best practices for ensuring the full reproducibility of AI research, covering everything from detailed training recipes and hyperparameter logging to pinning foundation model dependencies and reporting variance honesty. Essential for academic publication integrity.

A methodology guide for structuring high-stakes academic research experiments, particularly for top-tier CV conferences (like ICCV). It emphasizes systematic planning under strict deadlines, covering benchmark drift audits, ensuring fairness by accounting for pretraining confounds, structuring ablations to isolate mechanisms, and strategically sequencing runs to maximize impact before the submission deadline.

A comprehensive guide for academic authors detailing how to establish and maintain rigorous reproducibility in Machine Learning research papers (e.g., ICLR). It instructs on mapping every central claim to verifiable evidence, including recording seeds, variance, specific compute budgets (training vs. inference), data preprocessing scripts, and ethical considerations. The statement must function as a public contract for the scientific community.