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 pytorch-preference-optimizationgit 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/pytorch-preference-optimization)<a href="https://agentmods.dev/skills/cxcscmu/skilllearnbench/pytorch-preference-optimization"><img src="https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/pytorch-preference-optimization/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/cxcscmu/skilllearnbench/pytorch-preference-optimization"><img src="https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/pytorch-preference-optimization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00029 | $0.00297 |
| Opus 5.5 | $0.00012 | $0.00119 |
| Sonnet 5.5 | $0.00006 | $0.00059 |
| Haiku 4.5 | $0.00003 | $0.00030 |
Grade A, and why
pytorch-preference-optimization 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 1mo 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
PyTorch Preference Optimization Patterns
Key Functions
torch.nn.functional.logsigmoid(x): Numerically stable log-sigmoid, used in Bradley-Terry losses.torch.relu(x): Used for hinge-loss variants.torch.FloatTensor: Common type for log probability tensors.
Implementation Pattern
import torch
import torch.nn.functional as F
def preference_loss(chosen_logps, rejected_logps, beta, gamma, label_smoothing=0.0, loss_type="sigmoid"):
logits = beta * (chosen_logps - rejected_logps) - gamma
if loss_type == "sigmoid":
losses = -F.logsigmoid(logits) * (1 - label_smoothing) - F.logsigmoid(-logits) * label_smoothing
elif loss_type == "hinge":
losses = torch.relu(1 - logits)
chosen_rewards = beta * chosen_logps.detach()
rejected_rewards = beta * rejected_logps.detach()
return losses, chosen_rewards, rejected_rewards
Environment Setup
- trl==0.9.6 is needed for compatibility with older SimPO codebases (CPOTrainer import).
- PyTorch CPU is sufficient for loss computation testing.
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.
- 1mo ago First seen · 37 lines · 29 tokens per session scan A 78924966a723
pytorch-preference-optimization is a skill published in the GitHub repository cxcscmu/SkillLearnBench (91 stars, last pushed 19d ago), licensed MIT. It adds 29 tokens to every session and 297 once invoked, about $0.0001 per session on Opus 5.5. 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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