Agent Skill
Simpo Training skill
simpo-trainingOrchestra-Research/AI-Research-SKILLs06-post-training/simpo/SKILL.md
Simple Preference Optimization for LLM alignment. Reference-free alternative to DPO with better performance (+6.4 points on AlpacaEval 2.0). No reference model needed, more efficient than DPO. Use for preference alignment when want simpler, faster training than DPO/PPO.
always on
62 tokname + description
once used
1,531 tokSKILL.md body
bundled files
326 KB
repo stars
14kGitHub
Install the Simpo Training skill
Skills CLI (Claude Code, Codex, Cursor and others)
npx skills add https://github.com/Orchestra-Research/AI-Research-SKILLs --skill simpo-training
Claude Code, by hand (all projects)
git clone --depth 1 https://github.com/Orchestra-Research/AI-Research-SKILLs.git /tmp/AI-Research-SKILLs mkdir -p ~/.claude/skills cp -R /tmp/AI-Research-SKILLs/06-post-training/simpo ~/.claude/skills/simpo-training
For one project only, copy the folder into .claude/skills/ in that project instead. The agent picks it up when a task matches its description.
What it costs your context
Agent Skills load in three steps. Tokens counted with the o200k tokenizer; your model's count will differ a little.
| When | What loads | Cost |
|---|---|---|
| Every session | Name and description | 62 tok |
| When the agent uses it | The SKILL.md instructions | 1,531 tok |
| Only if opened | 3 bundled files (26 KB) | varies |
Until it is used, simpo-training costs about 22× less than the median MCP server we measured (1,362 tokens on every turn). Installing all 98 skills from Orchestra-Research/AI-Research-SKILLs would load 6,174 tokens of descriptions. Skills vs MCP study
Checks
- The name doesn't match its folder name, as the specification requires.
Checked 2026-10-22 · method
What's inside
The headings of its SKILL.md. Read the full file on GitHub.
- SimPO - Simple Preference Optimization
- Quick start
- Common workflows
- Workflow 1: Train from base model (Mistral 7B)
- Workflow 2: Fine-tune instruct model (Llama 3 8B)
- Workflow 3: Reasoning-intensive tasks (lower LR)
- When to use vs alternatives
- Common issues
- Advanced topics
- Hardware requirements
- Resources
More skills from Orchestra-Research/AI-Research-SKILLs
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Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper.
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Compiles any research input — PDF papers, GitHub repositories, experiment logs, code directories, or raw notes — into a complete Agent-Native Research Artifact (ARA) with cognitive layer (claims, concepts, heuristics), physical layer (configs, code stubs), exploration graph, and grounded evidence. Use when ingesting a paper or codebase into a structured, machine-executable knowledge package, building an ARA from scratch, or converting research outputs into a falsifiable, agent-traversable form.
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Records research provenance as a post-task epilogue, scanning conversation history at the end of a coding or research session to extract decisions, experiments, dead ends, claims, heuristics, and pivots, and writing them into the ara/ directory with user-vs-AI provenance tags. Use as a session epilogue — never during execution — to maintain a faithful, auditable trace of how a research project actually evolved.
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Performs ARA Seal Level 2 semantic epistemic review on Agent-Native Research Artifacts, scoring six dimensions (evidence relevance, falsifiability, scope calibration, argument coherence, exploration integrity, methodological rigor) and producing a constructive, severity-ranked report with a Strong Accept-to-Reject recommendation. Use after Level 1 structural validation passes, when an ARA needs an objective epistemic critique before publication or release.
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PyTorch library for audio generation including text-to-music (MusicGen) and text-to-sound (AudioGen). Use when you need to generate music from text descriptions, create sound effects, or perform melody-conditioned music generation.
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14k38 tok idle · 2,137 in use - autoresearchOrchestra-Research/AI-Research-SKILLs
Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.
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Activation-aware weight quantization for 4-bit LLM compression with 3x speedup and minimal accuracy loss. Use when deploying large models (7B-70B) on limited GPU memory, when you need faster inference than GPTQ with better accuracy preservation, or for instruction-tuned and multimodal models. MLSys 2024 Best Paper Award winner.
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