Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add cxcscmu/SkillLearnBench --skill simpo-lossgit clone --depth 1 https://github.com/cxcscmu/SkillLearnBenchWrote 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/cxcscmu/skilllearnbench/simpo-loss)<a href="https://agentmods.dev/skills/cxcscmu/skilllearnbench/simpo-loss"><img src="https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/simpo-loss.svg" alt="Measured on agentmods" 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 | $0.00022 | $0.00334 |
| Opus 5 | $0.00011 | $0.00167 |
| Sonnet 5 | $0.00004 | $0.00067 |
| Haiku 4.5 | $0.00002 | $0.00033 |
Grade A, and why
simpo-loss 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 3d 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.
What it actually says
SimPO Loss
Overview
SimPO is a reference-free preference optimization algorithm. Its key innovation is using the average log probability of a sequence as the implicit reward, plus a target reward margin γ.
Loss Formula (Eq. 6 from the paper)
L_SimPO = -E log σ(β/|yw| · log πθ(yw|x) - β/|yl| · log πθ(yl|x) - γ)
Since the log probabilities passed to simpo_loss are already length-normalized (average log prob), the loss simplifies to:
logits = β * policy_chosen_logps - β * policy_rejected_logps - γ
where γ = gamma_beta_ratio * beta.
Loss Types
- sigmoid (default):
losses = -log σ(logits) * (1 - label_smoothing) - log σ(-logits) * label_smoothing - hinge:
losses = relu(1 - logits)
Rewards
chosen_rewards = β * policy_chosen_logpsrejected_rewards = β * policy_rejected_logps
Default Hyperparameters
- β = 2.0
- gamma_beta_ratio = 0.25 (so γ = 0.5)
- label_smoothing = 0.0
- loss_type = "sigmoid"
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.
- 3d ago First seen · 40 lines · 22 tokens per session scan A 744a29e01e5d
simpo-loss is a skill published in the GitHub repository cxcscmu/SkillLearnBench (83 stars, last pushed 1mo ago), licensed MIT. It adds 22 tokens to every session and 334 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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