agents-meet-rl

agents-meet-rl is a Fine-tuning & training data skill for AI agents, published by thinkwee in the AgentsMeetRL repository on GitHub. Skillspace rates agents-meet-rl B (81/100) and it passed the safety scan; the rating is based on static analysis only.

Troubleshooter for agentic-RL training, evaluation, and experiment design on LLM agents (single or multi-agent, multi-turn, tool-augmented). Routes a user's symptom to fixes anchored in the corpus. TRIGGER when: user is training, evaluating, or designing experiments for an RL-trained LLM agent; symptoms like reward not moving, eval flat, KL/entropy/length blow-ups, retokenization drift, tool-call parse failures, credit assignment, async-rollout staleness, judge inconsistency, benchmark contamination, pass@k vs pass@1; choices about ablation, baseline, framework, algorithm, reward, or data curation; user names GRPO, PPO, DAPO, veRL, OpenRLHF, slime, AReaL, RAGEN, or similar. SKIP: generic supervised LLM fine-tuning with no RL component; classical RL theory or tabular RL; non-LLM agents. Distilled from the AgentsMeetRL awesome list, snapshot 2026-06-20.

Grade

B

Quality

81 / 100

Safety

Passed

GitHub stars

1,692

Unproven: rated by static analysis only, not yet compared head-to-head.

Our two independent classifiers differ on this skill's category, so treat the placement as provisional.

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