HACKATHON_GUIDELINES.md

Meta x PyTorch Hackathon - Round 1 Guidelines

Overview

• Event: Meta x PyTorch Hackathon by Scaler School of Technology
• Theme: Build OpenEnv environments (Reinforcement Learning)
• Registration: 14th March - 3rd April
• Round 1: 25th March - 8th April
• Submission Window Opens: 28th March
• Finale: 25th-26th April
• Submission Deadline: 8th April 2026, 11:59 PM (confirm timezone on dashboard)

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Team Structure

• Solo: Compete individually (locked for Round 1 only)
• Team: 2-3 members. Only team lead fills the team form.
• Once confirmed, teams cannot be changed.

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Round 1 Problem Statement

Build a complete, real-world OpenEnv environment that an AI agent can learn from through the standard step() / reset() / state() API.

Key Requirements

1. Must simulate a real-world task (not games or toys)
2. Implement full OpenEnv spec: typed models, step()/reset()/state(), openenv.yaml
3. Minimum 3 tasks with agent graders (easy → medium → hard, scores/reward 0.0–1.0)
4. Meaningful reward function with partial progress signals
5. Baseline inference script with reproducible scores
6. Deploy to Hugging Face Spaces + working Dockerfile
7. README with environment description, action/observation spaces, setup instructions

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Evaluation Criteria

Pre-Submission Checklist (All Must Pass)

Criteria	Description
HF Space deploys	Automated ping to Space URL must return 200 and respond to reset()
OpenEnv spec compliance	Validate openenv.yaml, typed models, step()/reset()/state() endpoints
Dockerfile builds	Automated docker build on submitted repo
Baseline reproduces	Run inference script — must complete without error and produce scores
3+ tasks with graders	Enumerate tasks, run each grader, verify scores/reward in 0.0–1.0 range

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Mandatory Additional Instructions

Environment Variables (Must be defined)

API_BASE_URL   # The API endpoint for the LLM
MODEL_NAME     # The model identifier to use for inference
HF_TOKEN       # Your Hugging Face API key

Inference Script Requirements

• Filename: Must be named inference.py in the root directory
• LLM Client: Must use OpenAI Client for all LLM calls
• Logging Format: Must emit structured stdout logs following [START], [STEP], and [END] format (field names, ordering, and formatting are strict)

Infrastructure Restrictions

• Runtime of inference script should be less than 20 minutes
• Must work on a machine with vCPU=2, memory=8GB

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Quick Checklist (Must-Haves)

• HF Space returns 200 and responds to reset()
• openenv.yaml validates; step()/reset()/state() endpoints respond correctly
• Dockerfile builds in CI
• inference.py runs end-to-end and produces scores
• 3+ tasks with graders; reward in 0.0–1.0 range
• OpenAI client used for all LLM calls; logs follow strict [START]/[STEP]/[END] format

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Preparatory Course (4 Modules ~3.5 hours)

Module	Title	Duration
1	Why OpenEnv?	45 min
2	Using Existing Environments	50 min
3	Deploying Environments	45 min
4	Building Your Own Environment	60 min

Note: Each module - read the README first, then open the notebook in Colab. No local setup needed.

Course Repository

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How to Submit

1. Complete Step 1 (Team/Solo selection)
2. Build your OpenEnv environment
3. Deploy to Hugging Face Spaces
4. Run pre-submission validation script
5. Submit via dashboard (only team leaders can submit)

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What Happens After Round 1

• Results announced: 10th April
• Finale: 25th-26th April

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Need Help?

• Email: help_openenvhackathon@scaler.com
• Discord: Join the community for announcements, mentor access, and team matching
• Discord Link: https://discord.gg/Dedhy5pkWD

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Example Problem Statement Format

"Build a real-world task environment (e.g., incident triage or logistics scheduling) with clearly defined tasks, automated graders, and reward logic using the OpenEnv framework."

Expected Deliverables:

• Create an environment an AI agent can interact with
• Define tasks with increasing difficulty
• Write graders that verify task completion
• Define reward logic for scoring
• Package using OpenEnv for automated evaluation

Evaluation Areas:

• Runtime correctness: Runs without errors
• Interface compliance: Follows OpenEnv standard
• Task design: Clear, realistic, testable
• Grading logic: Reward system makes sense

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Resources

• OpenEnv Course Repository
• Join Discord Community
• Contact Support

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Last Updated: April 3, 2026