docs/OpenEnv_Hackathon_README.md

🧠 OpenEnv Hackathon — Judging & Expectations Guide

🚨 TL;DR (What You Actually Need to Do)

Build an environment where an LLM can train and measurably improve at something meaningful, then:

• Show actual training
• Provide evidence (metrics, reward curves, comparisons)
• Tell a clear, compelling story

A messy but ambitious project with real training evidence beats a polished but shallow one.

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⚖️ Judging Criteria (Core Evaluation)

1. 🌟 Environment Innovation — 40%

• Is your environment novel, creative, or challenging?
• Does it meaningfully test agent behavior?
• Avoid overused ideas (grid worlds, chess clones, etc.)

2. 🎤 Storytelling & Presentation — 30%

• Clearly explain:
  • The problem
  • The environment
  • What the agent learned
• Demo should be engaging and easy to follow

3. 📈 Showing Improvement in Rewards — 20%

• Must prove learning happened
• Evidence:
  • Reward curves
  • Before vs after behavior
  • Baseline comparisons

4. ⚙️ Reward & Training Pipeline — 10%

• Reward logic should be coherent and hard to exploit
• Training should improve agent behavior

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📦 Minimum Submission Requirements

• Use OpenEnv (latest release)
• Provide a working training script (Unsloth or HuggingFace TRL)
• Show training evidence (loss + reward plots)
• Submit:
  • Mini-blog OR
  • <2 min video OR
  • Slides
• Host on Hugging Face Spaces
• Provide a README with problem, environment, results, links

Rules:

• One submission per team
• Submit environment URL
• No changes after deadline

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🧪 What Judges Look For

🔬 Real Training

• Training must run against your environment
• Show learning with plots, metrics, comparisons

🧠 Reward Design

• Dense and informative rewards
• Hard to game
• Avoid simple binary rewards

🚀 Ambitious Problems

• Solve something LLMs struggle with
• Prefer underexplored domains

📊 Clear Results

• Label axes properly
• Save plots as images
• Show comparisons clearly

📖 Tell a Story

Your README should answer:

1. Problem
2. Environment
3. Results
4. Why it matters

🧹 Clean Engineering

• Use OpenEnv properly
• Follow Gym API (reset, step, state)
• Maintain clean architecture

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🧭 Problem Selection Guidelines

• Reuse Round 1 idea only if it fits themes
• Build environment + reward model early
• Ensure alignment with judging criteria

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🏁 Final Advice

• Be ambitious
• Show real learning
• Communicate clearly

Judges want projects that push the frontier of LLM training.