alexzhang13

alexzhang13 / longcot-mini-rlm-results

Public

Storing the LongCoT-mini results for RLM(GPT-5.2)

12
0
100% credibility
Found Apr 27, 2026 at 12 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Jupyter Notebook
AI Summary

This repository shares evaluation data and a simple web viewer for visualizing an AI model's reasoning trajectories on the LongCoT-mini benchmark dataset across domains like logic, math, chemistry, chess, and computer science.

How It Works

1
🔍 Discover AI reasoning results

You stumble upon a shared collection of an AI's thought processes on tough puzzles in logic, math, chemistry, chess, and coding.

2
📥 Grab the files

You download the folder to your computer to explore these AI thinking examples up close.

3
🚀 Launch the viewer

You run a quick starter to open a friendly webpage right on your machine, bringing the results to life.

4
📂 Choose your interest

You pick a category like math or chess from the list to focus on relevant examples.

5
📋 Scan the examples

You browse a table of results, easily filtering to see full successes, partial wins, or interesting failures.

6
🔎 Dive into details

You click on a specific example to unfold the AI's full step-by-step reasoning, tools, and final answer.

✅ Unlock insights

You now clearly see how the AI tackled complex problems, spotting strengths, mistakes, and patterns in its thinking.

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Star Growth

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AI-Generated Review

What is longcot-mini-rlm-results?

This repo stores trajectories and results from running RLM (GPT-5.2) on the LongCoT-mini benchmark for long-context reasoning tasks across domains like logic, math, chemistry, chess, and CS. Built around Jupyter Notebook with a Python Flask web viewer, it lets you inspect prompts, multi-turn interactions, tool calls, rewards, and final answers interactively. Fire up the viewer with a simple `python app.py` command to browse at localhost:5050—no setup hassle for github storing data, images, or large trajectory files.

Why is it gaining traction?

Unlike raw JSON dumps or static tables, it offers a filterable table of results (by failures or partial rewards) and drill-down views of full trajectories, making it dead simple to spot where models fail on long prompts stored in github repositories. The patched HM rewards fix a common eval bug, ensuring accurate scoring out of the box. Developers dig the client-side JS controls for expanding reasoning steps or toggling turns without reloading.

Who should use this?

AI researchers fine-tuning RLMs on LongCoT or LongCoT-mini who need to debug trajectories beyond aggregated metrics. Eval engineers storing prompts in github repositories or comparing RLM(GPT-5.2) runs on mini benchmarks. Teams storing notes on github or github storing large files from long-context experiments.

Verdict

Grab it if you're deep in LongCoT evals— the viewer delivers real inspection value fast, despite 12 stars and 1.0% credibility score signaling early maturity with thin docs. Skip for general use; it's hyper-niche but solid for its lane.

(178 words)

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