Individual skill

evo-simpo-code-reproduction

Implements the SimPO (Simple Preference Optimization) loss function and handles environment setup, execution, and result saving for code reproduction tasks.

tasks-evolved/simpo-code-reproduction/environment/skills/evo-simpo-code-reproduction/SKILL.md

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Repository stars (not skill ratings): 98
Repository last updated: Sep 10, 2026
License: Apache-2.0

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# evo-simpo-code-reproduction

## Overview
Complete skill for reproducing the SimPO (Simple Preference Optimization) loss function from the paper by Meng et al. (2024). Handles implementation of the `simpo_loss` method, environment setup, unit test execution, and saving results.

## Key Concepts (from the paper)

### SimPO Loss Formula
```
L_SimPO = -log(sigmoid(beta/|y_w| * sum(log π(y_w|x)) - beta/|y_l| * sum(log π(y_l|x)) - gamma))
```

### Critical Implementation Details
1. **Inputs are pre-normalized**: `policy_chosen_logps` and `policy_rejected_logps` are already length-averaged (mean log probs per token) before entering `simpo_loss`. The averaging happens in `get_batch_logps` with `average_log_prob=True`.