README.md

metadata

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