AI Research Skills Library is a collection of reusable instructions that guide AI agents through research and machine-learning engineering tasks, from finding ideas and writing papers to training, evaluation, and deployment. It is for configuring agents such as Claude Code, Codex, and Gemini to perform research workflows.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/Orchestra-Research/AI-Research-SKILLsnpx agentmods add skills/orchestra-research/ai-research-skills/torchforgeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/orchestra-research/ai-research-skills/torchforge)<a href="https://agentmods.dev/skills/orchestra-research/ai-research-skills/torchforge"><img src="https://agentmods.dev/badge/skills/orchestra-research/ai-research-skills/torchforge/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/orchestra-research/ai-research-skills/torchforge"><img src="https://agentmods.dev/badge/skills/orchestra-research/ai-research-skills/torchforge.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00049 | $0.02611 |
| Opus 5 | $0.00024 | $0.01306 |
| Sonnet 5 | $0.00010 | $0.00522 |
| Haiku 4.5 | $0.00005 | $0.00261 |
Grade A, and why
torchforge-rl-training scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to torchforge-rl-training — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 434 lines — stays where its author put it; the contents beside it link to each section on GitHub.
torchforge: PyTorch-Native Agentic RL Library
torchforge is Meta's PyTorch-native RL library that separates infrastructure concerns from algorithm concerns. It enables rapid RL research by letting you focus on algorithms while handling distributed training, inference, and weight sync automatically.
When to Use torchforge
Choose torchforge when you need:
- Clean separation between RL algorithms and infrastructure
- PyTorch-native abstractions (no Ray dependency)
- Easy algorithm experimentation (GRPO, DAPO, SAPO in ~100 lines)
- Scalable training with Monarch actor system
- Integration with TorchTitan for model parallelism
Consider alternatives when:
- You need production-ready stability → use miles or verl
- You want Megatron-native training → use slime
- torchforge is experimental and APIs may change
Key Features
- Algorithm isolation: Implement RL algorithms without touching infrastructure
- Scalability: From single GPU to thousands via Monarch
- Modern stack: TorchTitan (training), vLLM (inference), TorchStore (sync)
- Loss functions: GRPO, DAPO, CISPO, GSPO, SAPO built-in
Architecture Overview
┌─────────────────────────────────────────────────────────┐
│ Application Layer (Your Code) │
│ - Define reward models, loss functions, sampling │
└─────────────────────┬───────────────────────────────────┘
│
┌─────────────────────▼───────────────────────────────────┐
│ Forge API Layer │
│ - Episode, Group dataclasses │
│ - Service interfaces (async/await) │
└─────────────────────┬───────────────────────────────────┘
│
┌─────────────────────▼───────────────────────────────────┐
│ Distributed Services (Monarch) │
│ ├── Trainer (TorchTitan FSDP) │
│ ├── Generator (vLLM inference) │
│ ├── Reference Model (frozen KL baseline) │
│ └── Reward Actors (compute rewards) │
└─────────────────────────────────────────────────────────┘
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 434 lines · 49 tokens per session scan A 809da22c47d5
torchforge-rl-training is a skill published in the GitHub repository Orchestra-Research/AI-Research-SKILLs (12,508 stars, last pushed 2mo ago), licensed MIT. It adds 49 tokens to every session and 2,611 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to torchforge-rl-training, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
torchforge-rl-training
Provides guidance for PyTorch-native agentic RL using torchforge, Meta's library separating infra from algorithms. Use when you want clean RL abstractions, easy algorithm experimentation, or scalable training with Monarch and TorchTitan.
torchforge-rl-training
Provides guidance for PyTorch-native agentic RL using torchforge, Meta's library separating infra from algorithms. Use when you want clean RL abstractions, easy algorithm experimentation, or scalable training with Monarch and TorchTitan.
torchforge-rl-training
Provides guidance for PyTorch-native agentic RL using torchforge, Meta's library separating infra from algorithms. Use when you want clean RL abstractions, easy algorithm experimentation, or scalable training with Monarch and TorchTitan.
torchforge-rl-training
Provides guidance for PyTorch-native agentic RL using torchforge, Meta's library separating infra from algorithms. Use when you want clean RL abstractions, easy algorithm experimentation, or scalable training with Monarch and TorchTitan.
fine-tuning-with-trl
Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.
fine-tuning-with-trl
Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.