simpo-loss SKILL.md skillA
simpo-loss from cxcscmu/skilllearnbench is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add cxcscmu/skilllearnbench/simpo-loss@git:20260424.d50cf5aPins git:20260424.d50cf5a in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- cxcscmu/skilllearnbench
- Path
skills/b1-one-shot-claude-sonnet-4-6/nlp-paper-reproduction/simpo-loss/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260424.d50cf5a, committed Apr 24, 2026
- Size
- 2,553 bytes
- Repository stars
- 91
- License
- MIT
Implements the SimPO (Simple Preference Optimization) loss function from the paper "SimPO: Simple Preference Optimization with a Reference-Free Reward". Use this when implementing or understanding SimPO training objectives.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# SimPO Loss Implementation
## Overview
SimPO (Simple Preference Optimization) is a reference-free preference optimization method that:
1. Uses length-normalized log probability as the implicit reward (aligns with generation metric)
2. Introduces a target reward margin γ to separate winning/losing responses
3. Eliminates the need for a reference model (unlike DPO)
## Loss Formula (from paper Eq. 6)
```
LSimPO(πθ) = −E(x,yw,yl)∼D [ log σ( β/|yw| * log πθ(yw|x) - β/|yl| * log πθ(yl|x) - γ ) ]
```
Where:
- β: scaling factor (default: 2.0)
- γ: target reward margin (default: gamma_beta_ratio * β = 0.25 * 2.0 = 0.5)
- |yw|, |yl|: lengths of winning/losing responses
- policy_chosen_logps, policy_rejected_logps: AVERAGE log probabilities per token
## Key Notes
- `policy_chosen_logps` = average log prob per token = (1/|yw|) * log πθ(yw|x)
- So `β * policy_chosen_logps` = `β/|yw| * log πθ(yw|x)` (already normalized)
- gamma_beta_ratio = γ/β, so γ = gamma_beta_ratio * β
## Implementation
```python
def simpo_loss(
self,
policy_chosen_logps: torch.FloatTensor,
policy_rejected_logps: torch.FloatTensor,
) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
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How to install
mdr add cxcscmu/skilllearnbench/simpo-loss@sha256:188fb4781b731219The line at the top of this page pins the label git:20260424.d50cf5a, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_f6o6e2ryjsohpq4i)
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Versions
The registry has recorded 1 version of this file, committed on Apr 24, 2026.
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Source
cxcscmu/skilllearnbench · 91 stars · license MIT · pushed 2026-10-02 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_f6o6e2ryjsohpq4i GET https://markdownregistry.com/api/v1/resolve?ref=cxcscmu/skilllearnbench/simpo-loss GET https://markdownregistry.com/api/v1/blob/188fb4781b73121976dde7e27b63340484356741b31d165e4024d7a1a09a1f28
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.
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