Troubleshooter + machine-readable corpus for reinforcement-learning training, evaluation, and experiment design on LLM agents (single/multi-agent, multi-turn, tool-augmented). Distilled from the AgentsMeetRL awesome list (378 projects).
Knowledge base compiled from OpenAI's Spinning Up in Deep RL (MIT, Joshua Achiam) by engineering/book-to-skill. A resident core carries the RL optimization problem, the model-free taxonomy, the policy-gradient template with its five valid weights, the safe-step family (VPG to TRPO to PPO) and the overestimation family (DDPG to TD3 and SAC); 20 on-demand chapters cover key concepts and MDPs, the algorithm taxonomy and model bias, the policy gradient derivation with the log-derivative trick and EGLP lemma, Achiam's researcher essay, the key-papers topic map, the exercises including the silent DDPG broadcasting bug, the benchmark parity disclosure, one chapter per algorithm, and the logger/MPI/ExperimentGrid utilities. Ships a glossary, a patterns file with 16 techniques, a decision cheatsheet with thresholds, a cs-spinning-up-deep-rl agent and /cs-spinning-up-deep-rl. Structured study notes, not a reproduction of the source.
RLHF and preference alignment including TRL, GRPO, OpenRLHF, SimPO, verl, slime, miles, and torchforge. Use when aligning models with human preferences, training reward models, or large-scale RL training.
Install, operate, and troubleshoot Unsloth Core, Studio, and Desktop for local inference and model training. Use for Unsloth environment setup, LoRA/QLoRA supervised fine-tuning, coding specialization, verifier audits, chat templates, GRPO and preference training, vision and audio workflows, decision models, local coding agents and tools, dynamic quantization, checkpoint recovery, multi-GPU training, and model export to GGUF or serving engines. Do not use for framework-neutral ML strategy or operating a standalone inference server unrelated to Unsloth.
LLM post-training — unified interface for SFT, OSFT, LoRA fine-tuning, and GRPO reinforcement learning
4 skills
Red-Hat-AI-Innovation-Team/training_hub 105
Maintain agents-meet-rl?
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