---
title: Weak Supervision Reasoning Explorer
emoji: 🔬
colorFrom: purple
colorTo: pink
sdk: gradio
sdk_version: 4.36.0
app_file: app.py
pinned: false
---

# Weak Supervision Reasoning Explorer

Interactive demo exploring when LLMs can learn to reason with weak supervision, based on paper 2604.18574.

**Hypothesis:** Models that generalize under weak supervision exhibit a prolonged pre-saturation phase during which training reward and downstream performance climb together, while rapid saturation indicates memorization.

## Key Findings from Paper

- **Reward Saturation Dynamics:** Models that generalize show prolonged pre-saturation
- **Reasoning Faithfulness:** Intermediate steps logically supporting final answers predict generalization
- **SFT is Critical:** Supervised fine-tuning on explicit reasoning traces enables weak supervision generalization

## Features

- Visualize reward saturation curves
- Compare reasoning faithfulness across models
- Interactive weak supervision scenarios