| name | agents-meet-rl |
| description | 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 2026-05-23. |
What this is
A corpus-anchored handbook for diagnosis and selection. It supplies
knowledge — it does not read or run your training: it can't inspect
your logs, wandb, or live metrics. You bring the symptom; it returns
likely causes, checks, and cited fixes for you to apply.
Where things are
problems/_INDEX.md — symptom → file routing, grouped under
training/, evaluation/, research-workflow/. Start here.problems/<cat>/<file>.md — per-symptom files. Most follow
Symptoms → Root causes → Diagnosis → Fixes → References; knob /
decision / modality / eval-checklist / research-workflow files use
task-oriented structures.references/_INDEX.md + references/<cat>.md — per-category
project lists with full metadata. Each entry carries an Idea:
line — one sentence on its distinctive contribution, grounded in the
paper/repo. Use for "which framework / benchmark" selection, to look
up project names not routed via problems/_INDEX.md, and to answer
"what's the idea behind X" by quoting its Idea: line.database.json — machine-readable, 312 entries (each with a
takeaway field mirroring the Idea: line) plus 3 paper-only
algorithms (DAPO, Dr.GRPO, VAPO) whitelisted in
scripts/lint_skill.py.
Citing fixes
Name the algorithm or idea, then anchor with whatever canonical URLs
exist for that entry — typically github + arxiv + org + date, but
paper-only algorithms (in the whitelist) get just the paper URL, and
tools / environments without papers get just github + org + date.
Examples:
Project with paper (typical):
Adapt Search-R1's outcome-only reward —
code ·
paper · UIUC/Google · 2025.3.
Paper-only algorithm (whitelist):
Try DAPO's clip-higher —
paper · ByteDance Seed · 2025.3.
Tool / environment without paper:
Run rollouts in atropos —
code · Nous Research · 2025.4.
Cite at the idea level, not paper sections or file paths inside
repos — they rot. If an entry isn't in the corpus, say so; don't
fabricate.
If two corpus entries share a name (e.g. ARPO appears as both a
reasoning RL method and a GUI-agent training method), disambiguate by
including the org and paper URL — they are different works.
Staleness
Snapshot date: 2026-05-23. If the user mentions a project or paper
released after that, flag explicitly that this skill's corpus may not
cover it.
