README.md

metadata

title: ThinkWhileThinking
emoji: 🧠
colorFrom: purple
colorTo: indigo
sdk: gradio
sdk_version: 5.33.0
app_file: app.py
pinned: true
license: mit
tags:
  - build-small-hackathon
  - thousand-token-wood
  - tiny-titan
  - track:backyard
  - sponsor:nvidia
  - sponsor:modal
  - achievement:offgrid
  - achievement:welltuned
  - achievement:offbrand
  - achievement:sharing

🧠 ThinkWhileThinking

Real-time reasoning failure detection in small language models via step-level process supervision

• 🎥📝 Demo Video & Technical Write-up: Watch the Demo & Read the Architecture on X

What is this?

Most interpretability research is post-hoc — you find out why a model was wrong after it's already wrong. ThinkWhileThinking flips this:

A fine-tuned Step Probe model watches a reasoner's chain-of-thought in real time, and predicts — at each reasoning step — whether the logic is about to go wrong, before the final answer is revealed.

How it works

User inputs a math / logic problem
        ↓
Reasoner (Qwen2.5-1.5B-Instruct) generates chain-of-thought step by step
        ↓
Step Probe (fine-tuned Qwen2.5-0.5B) scores each step in real time
        ↓
UI highlights exactly where reasoning starts to break down
        ↓
Reasoning Health score computed across all steps

The Research Contribution

Research Question: Can a lightweight probe model, trained only on step-level correctness annotations, predict mid-reasoning whether the final answer will be correct — before seeing the answer?

Unlike prior work on CoT monitoring (which focuses on safety, deception, or adversarial attacks), TWT addresses correctness failure prediction at inference time using lightweight process supervision — making real-time interpretability accessible without frontier-scale models.

Models Used

Role	Model	Parameters
Reasoner	Qwen/Qwen2.5-1.5B-Instruct	1.5B
Step Probe	realArceus/twt-probe	0.5B
Total		2B ≤ 32B ✅

Training

The Step Probe (realArceus/twt-probe) is a fine-tuned Qwen2.5-0.5B-Instruct trained as a binary sequence classifier on PRM800K (trl-lib/prm800k) — OpenAI's process reward model dataset with step-level human correctness annotations.

Detail	Value
Dataset	PRM800K (trl-lib/prm800k)
Samples	40,000 step-level pairs
Task	Binary classification: correct vs. faulty step
Hardware	Modal A10G GPU
Epochs	3
eval_accuracy	98.15%
eval_f1	99.05%

How is this different from PRMs?

Process Reward Models (PRMs) assign quality scores to steps for training signal. TWT uses step-level supervision for real-time inference-time interpretability — a fundamentally different use case. We also demonstrate this works at 0.5B scale, far smaller than typical PRM

Built By

Krishna Pahuja — Final year AI student · HF Build Small Hackathon 2026

License

MIT