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agents-meet-rl

النجوم١٬٧١١
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آخر تحديث٢٠ يونيو ٢٠٢٦ في ٠٩:٠١

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.

التثبيت

التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.

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92 ملفات
SKILL.md
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