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Guide for hyperparameter selection — learning rate formulas, LoRA rank, batch size, group size, schedules, and model-specific tuning. Use when the user asks about learning rate, batch size, hyperparameter tuning, or how to configure training parameters.
hyperparams is an agent skill entry in ChatHome Skills, sourced from GitHub Skill Search and scored 58/100. # Hyperparameter Selection Guide for choosing training hyperparameters across SL, RL, DPO, and distillation. ## Reference - `docs/supervised-learning/sl-hyperpa. As an agent skill, it packages a reusable instruction set that AI coding agents such as Codex and Claude Code can load to perform this task without hand-written glue code. ChatHome Skills discovered this entry through its daily ingest of official registries and GitHub discovery queries, and re-verifies repository facts, freshness signals, and install metadata on every run. The quality score weighs official-registry presence, a working install command, license clarity, GitHub star trajectory, and repository freshness — the full formula is on the Sources page. The install command above is ready to copy. Review the last-pushed date and repository status before adding hyperparams to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.
codex skill install uiuc-kang-lab/rlvr_generalization_boundsReview the upstream source before running this command.
Scan coverage: Install metadata and the indexed document body.
Automated signals from install metadata and a bounded skill body — not a full code security audit and not a recursive source or dependency review.
# Hyperparameter Selection Guide for choosing training hyperparameters across SL, RL, DPO, and distillation. ## Reference - `docs/supervised-learning/sl-hyperparams.mdx` — SL hyper