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| Join Discord |
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| Registration |
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| 14th March - 3rd April |
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| Declaration |
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| Before R1 |
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| Prepare |
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| Now - 25th March |
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| Round 1 |
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| 25th March - 8th April |
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| Results |
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| 10th April |
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| Finale |
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| 25th-26th April |
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| Welcome RONIT RAJ! |
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| ronitk964@gmail.com |
| Copy |
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| Join the Discord Community |
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| All announcements, mentor access, and team matching happens here. |
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| Join Discord |
| QUICK TOGGLe |
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| Team form Submission |
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| Preparatory Course |
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| Start Assessment |
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| FAQs |
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| step 1 |
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| How will you compete? |
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| Choose solo or team before you can start the assessment |
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| Step 1 Complete |
| Team: AlphaQ |
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| 👤 |
| RONIT RAJ |
| ronitk964@gmail.com |
| Team Lead |
| 👤 |
| Murtuza Shaikh |
| murtuzashaikh.2023@gmail.com |
| Accepted |
| 👤 |
| Khushi Singh |
| khushisingh82072@gmail.com |
| Accepted |
| 🔒 |
| Team is permanently locked. Changes are not allowed after confirmation. |
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| OpenEnv Round 1 Bootcamp |
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| OpenEnv Round 1 Bootcamp |
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| OpenEnv Round 1 Bootcamp |
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| OpenEnv Round 1 Bootcamp |
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| OpenEnv Round 1 Bootcamp |
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| OpenEnv Round 1 Bootcamp |
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| OpenEnv Round 1 Bootcamp |
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| OpenEnv Round 1 Bootcamp |
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| OpenEnv Round 1 Bootcamp |
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| OpenEnv Round 1 Bootcamp |
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| OpenEnv Round 1 Bootcamp |
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| OpenEnv Round 1 Bootcamp |
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| OpenEnv Round 1 Bootcamp: Build Your First RL Environment |
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| Live walkthrough to submit a strong Round 1 entry |
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| timing |
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| 8:00 PM Onwards |
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| Wednesday, 1st April |
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| Host |
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| Ben Burtenshaw |
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| Community Education in AI at Hugging Face |
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| Pulkit Aneja |
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| Scaler Instructor |
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| Watch Recording |
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| PROBLEM STATEMENT |
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| Round 1 — Problem Statement |
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| The Task |
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| Build a complete, real-world OpenEnv environment that an AI agent can learn from through the standard step() / reset() / state() API. |
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| Key Requirements at a Glance |
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| Must simulate a real-world task (not games or toys) |
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| Implement full OpenEnv spec: typed models, step()/reset()/state(), openenv.yaml |
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| Minimum 3 tasks with agent graders (easy → medium → hard, scores/reward 0.0–1.0) |
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| Meaningful reward function with partial progress signals |
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| Baseline inference script with reproducible scores |
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| Deploy to Hugging Face Spaces + working Dockerfile |
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| README with environment description, action/observation spaces, setup instructions |
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| Functional Requirements |
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| Real-world task simulation |
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| The environment must simulate a task humans actually do. Not games, not toys. Examples: email triage, code review, data cleaning, scheduling, customer support, content moderation. |
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| OpenEnv spec compliance |
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| Implement the full OpenEnv interface: typed Observation, Action, and Reward Pydantic models. step(action) → returns observation, reward, done, info. reset() → returns initial observation. state() → returns current state. openenv.yaml with metadata. Tested via openenv validate. |
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| Minimum 3 tasks with agent graders |
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| Each task defines a concrete objective an agent must accomplish, with a programmatic grader that scores performance (0.0–1.0). Tasks should range: easy → medium → hard. Graders must have clear, deterministic success/failure criteria. |
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| Meaningful reward function |
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| Provides signal over the full trajectory (not just binary end-of-episode). Rewards partial progress toward task completion. Penalizes clearly undesirable behavior (e.g. infinite loops, destructive actions). |
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| Baseline inference script |
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| Uses the OpenAI API client to run a model against the environment. Reads API credentials from environment variables (OPENAI_API_KEY). Produces a reproducible baseline score on all 3 tasks. |
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| Detailed Requirements |
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| Non-Functional Requirements |
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| Deploys to a Hugging Face Space |
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| Environment must run as a containerized HF Space tagged with openenv. |
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| Containerized execution |
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| Must include a working Dockerfile. The environment should start cleanly with docker build + docker run. |
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| Documentation |
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| README must include: environment description and motivation, action and observation space definitions, task descriptions with expected difficulty, setup and usage instructions, baseline scores. |
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| Parameter |
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| Weight |
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| Description |
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| Real-world utility |
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| 30% |
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| Does the environment model a genuine task? Would someone actually use this to train or evaluate agents? |
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| Task & grader quality |
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| 25% |
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| Are tasks well-defined with clear objectives? Do graders accurately and fairly measure success? Meaningful difficulty progression? |
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| Environment design |
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| 20% |
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| Clean state management, sensible action/observation spaces, good reward shaping, proper episode boundaries. |
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| Code quality & spec compliance |
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| 15% |
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| Follows OpenEnv spec, clean project structure, typed models, documented, tested, Dockerfile works. |
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| Creativity & novelty |
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| 10% |
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| Novel problem domain, interesting mechanics, clever reward design, original approach. |
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| Scoring Breakdown |
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| Real-world utility (30%) |
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| • 0–5: Toy/artificial problem with no practical application |
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| • 6–15: Valid domain but shallow modeling of the real task |
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| • 16–25: Good domain modeling, would be useful for agent evaluation |
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| • 26–30: Excellent — fills a real gap, immediate value for the RL/agent community |
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| Task & grader quality (25%) |
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| • 3+ tasks with difficulty range? |
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| • Graders produce scores between 0.0–1.0? |
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| • Graders deterministic and reproducible? |
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| • Hard task genuinely challenges frontier models? |
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| Environment design (20%) |
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| • reset() produces clean state? |
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| • Action/observation types well-designed and documented? |
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| • Reward function provides useful varying signal (not just sparse)? |
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| • Episode boundaries sensible? |
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| Code quality & spec compliance (15%) |
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| • openenv validate passes? |
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| • docker build && docker run works? |
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| • HF Space deploys and responds? |
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| • Baseline script runs and reproduces scores? |
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| Creativity & novelty (10%) |
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| • Domain we haven’t seen in OpenEnv before? |
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| • Reward design has interesting properties? |
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| • Clever mechanics that make the environment engaging? |
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| Evaluation Criteria |
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| Phase 1: Automated Validation |
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| Pass/fail gate — HF Space deploys, OpenEnv spec compliance, Dockerfile builds, baseline reproduces, 3+ tasks with graders. |
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| Phase 2: Agentic Evaluation |
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| Scored — baseline agent re-run, standard Open LLM agent (e.g. Nemotron 3 Super) run against all environments, score variance check. |
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| Phase 3: Human Review |
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| Top submissions reviewed by Meta and Hugging Face engineers for real-world utility, creativity, and exploit checks. |
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| Disqualification Criteria |
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| Environment does not deploy or respond |
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| Plagiarized or trivially modified existing environments |
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| Graders that always return the same score |
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| No baseline inference script |
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| How Judging works |
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| Pre-Submission Checklist — all must pass or you're disqualified |
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| HF Space deploys |
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| Automated ping to the Space URL — must return 200 and respond to reset() |
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| OpenEnv spec compliance |
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| Validate openenv.yaml, typed models, step()/reset()/state() endpoints |
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| Dockerfile builds |
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| Automated docker build on the submitted repo |
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| Baseline reproduces |
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| Run the submitted inference script — must complete without error and produce scores |
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| 3+ tasks with graders |
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| Enumerate tasks, run each grader, verify scores/reward in 0.0–1.0 range |
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| Mandatory Additional Instructions |
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| Before submitting, ensure the following variables are defined in your environment configuration: |
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| API_BASE_URL The API endpoint for the LLM. |
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| MODEL_NAME The model identifier to use for inference. |
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| HF_TOKEN Your Hugging Face / API key. |
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| The inference script must be named `inference.py` and placed in the root directory of the project |
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| Participants must use OpenAI Client for all LLM calls using above variables |
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| Participants must emit structured stdout logs strictly following the [START], [STEP], and [END] format defined in the sample inference.py provided below. Any deviation in field names, ordering, or formatting will result in incorrect evaluation scoring. Refer to the Sample Inference Script for the complete format specification and examples. |
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| Infra Restrictions |
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| Runtime of inference script should be less than 20min |
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| Make sure your env and inference can run on a machine with vcpu=2, memory=8gb |
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| Validator |
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| Run the pre-submission validation script before submitting |
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| NEW |
| Sample Inference Script |
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| """ |
| [END] success=<true|false> steps=<n> score=<score> rewards=<r1,r2,...,rn> |
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| Rules: |
| - One [START] line at episode begin. |
| - One [STEP] line per step, immediately after env.step() returns. |
| - One [END] line after env.close(), always emitted (even on exception). |
| - reward and rewards are formatted to 2 decimal places. |
| - done and success are lowercase booleans: true or false. |
| - error is the raw last_action_error string, or null if none. |
| - All fields on a single line with no newlines within a line. |
| - Each tasks should return score in [0, 1] |
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| Example: |
| [START] task=click-test env=miniwob model=Qwen3-VL-30B |
| [STEP] step=1 action=click('123') reward=0.00 done=false error=null |
| [STEP] step=2 action=fill('456','text') reward=0.00 done=false error=null |
| [STEP] step=3 action=click('789') reward=1.00 done=true error=null |
| [END] success=true steps=3 score=1.00 rewards=0.00,0.00,1.00 |
| """ |
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| import asyncio |
| import os |
| import textwrap |
| from typing import List, Optional |
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| from openai import OpenAI |
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| from my_env_v4 import MyEnvV4Action, MyEnvV4Env |
| IMAGE_NAME = os.getenv("IMAGE_NAME") # If you are using docker image |
| API_KEY = os.getenv("HF_TOKEN") or os.getenv("API_KEY") |
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| API_BASE_URL = os.getenv("API_BASE_URL") or "https://router.huggingface.co/v1" |
| MODEL_NAME = os.getenv("MODEL_NAME") or "Qwen/Qwen2.5-72B-Instruct" |
| TASK_NAME = os.getenv("MY_ENV_V4_TASK", "echo") |
| BENCHMARK = os.getenv("MY_ENV_V4_BENCHMARK", "my_env_v4") |
| MAX_STEPS = 8 |
| TEMPERATURE = 0.7 |
| MAX_TOKENS = 150 |
| SUCCESS_SCORE_THRESHOLD = 0.1 # normalized score in [0, 1] |
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| # Max possible reward: each token contributes 0.1, across all steps |
| _MAX_REWARD_PER_STEP = MAX_TOKENS * 0.1 |
| MAX_TOTAL_REWARD = MAX_STEPS * _MAX_REWARD_PER_STEP |
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| SYSTEM_PROMPT = textwrap.dedent( |
| """ |
| You are interacting with a simple echo environment. |
| Each turn you must send a message. The environment will echo it back. |
| Reward is proportional to message length: reward = len(message) * 0.1 |
| Your goal is to maximize total reward by sending meaningful, substantive messages. |
| Reply with exactly one message string — no quotes, no prefixes, just the message text. |
| """ |
| ).strip() |
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| def log_start(task: str, env: str, model: str) -> None: |
| print(f"[START] task={task} env={env} model={model}", flush=True) |
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| def log_step(step: int, action: str, reward: float, done: bool, error: Optional[str]) -> None: |
| error_val = error if error else "null" |
| done_val = str(done).lower() |
| print( |
| f"[STEP] step={step} action={action} reward={reward:.2f} done={done_val} error={error_val}", |
| flush=True, |
| ) |
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| def log_end(success: bool, steps: int, score: float, rewards: List[float]) -> None: |
| rewards_str = ",".join(f"{r:.2f}" for r in rewards) |
| print(f"[END] success={str(success).lower()} steps={steps} score={score:.3f} rewards={rewards_str}", flush=True) |
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| def build_user_prompt(step: int, last_echoed: str, last_reward: float, history: List[str]) -> str: |
| history_block = "\n".join(history[-4:]) if history else "None" |
| return textwrap.dedent( |
| f""" |
| Step: {step} |
| Last echoed message: {last_echoed!r} |
| Last reward: {last_reward:.2f} |
| Previous steps: |
| {history_block} |
| Send your next message. |
| """ |
| ).strip() |
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| NEW |
| Pre Validation Script |
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| #!/usr/bin/env bash |
| # |
| # validate-submission.sh — OpenEnv Submission Validator |
| # |
| # Checks that your HF Space is live, Docker image builds, and openenv validate passes. |
| # |
| # Prerequisites: |
| # - Docker: https://docs.docker.com/get-docker/ |
| # - openenv-core: pip install openenv-core |
| # - curl (usually pre-installed) |
| # |
| # Run: |
| # curl -fsSL https://raw.githubusercontent.com/<owner>/<repo>/main/scripts/validate-submission.sh | bash -s -- <ping_url> [repo_dir] |
| # |
| # Or download and run locally: |
| # chmod +x validate-submission.sh |
| # ./validate-submission.sh <ping_url> [repo_dir] |
| # |
| # Arguments: |
| # ping_url Your HuggingFace Space URL (e.g. https://your-space.hf.space) |
| # repo_dir Path to your repo (default: current directory) |
| # |
| # Examples: |
| # ./validate-submission.sh https://my-team.hf.space |
| # ./validate-submission.sh https://my-team.hf.space ./my-repo |
| # |
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| set -uo pipefail |
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| DOCKER_BUILD_TIMEOUT=600 |
| if [ -t 1 ]; then |
| RED='\033[0;31m' |
| GREEN='\033[0;32m' |
| YELLOW='\033[1;33m' |
| BOLD='\033[1m' |
| NC='\033[0m' |
| else |
| RED='' GREEN='' YELLOW='' BOLD='' NC='' |
| fi |
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| run_with_timeout() { |
| local secs="$1"; shift |
| if command -v timeout &>/dev/null; then |
| timeout "$secs" "$@" |
| elif command -v gtimeout &>/dev/null; then |
| gtimeout "$secs" "$@" |
| else |
| "$@" & |
| local pid=$! |
| ( sleep "$secs" && kill "$pid" 2>/dev/null ) & |
| local watcher=$! |
| wait "$pid" 2>/dev/null |
| local rc=$? |
| kill "$watcher" 2>/dev/null |
| wait "$watcher" 2>/dev/null |
| return $rc |
| fi |
| } |
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| portable_mktemp() { |
| Submission window opens on 28th March |
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| Deadline: 8 Apr 11:59 PM |
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| Submit your Assessment |
| → |
| Study material |
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| Preparatory Course |
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| 4 modules · ~3.5 hours |
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| Each module: read the README first, then open the notebook in Colab. No local setup needed. |
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| Module 1: Why OpenEnv? |
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| ESSENTIAL FOR ROUND 1 |
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| 45 min |
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| Module 2: Using Existing Environments |
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| ESSENTIAL FOR ROUND 1 |
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| 50 min |
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| Module 3: Deploying Environments |
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| ESSENTIAL FOR ROUND 1 |
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| 45 min |
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| Module 4: Building Your Own Environment |
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| MOST IMPORTANT FOR ROUND 1 |
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| 60 min |
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| View full course repository |
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| GUIDE |
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| Round 1 Guide |
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| What to Expect |
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| Prerequisites |
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| How to Submit |
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| When Round 1 opens, you'll choose 1 of 4–5 problem statements and build an OpenEnv environment around it. |
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| Example of what a problem statement looks like |
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| "Build a mini-game RL environment with clearly defined tasks, automated graders, and reward logic using the OpenEnv framework." |
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| → Create a mini-game an AI agent can play |
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| → Define tasks with increasing difficulty |
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| → Write graders that verify task completion |
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| → Define reward logic for scoring |
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| → Package using OpenEnv for automated evaluation |
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| Evaluation Criteria |
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| Runtime correctness |
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| Runs without errors |
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| Interface compliance |
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| Follows OpenEnv standard |
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| Task design |
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| Clear, realistic, testable |
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| Grading logic |
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| Reward system makes sense |
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| Step 2 |
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| Submit your Assessment |
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| Complete Step 1 first |
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| Problem Statement is live. Build and submit. |
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| Round 1 begins |
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| Submission window opens on 28th March |
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| Deadline: 8 Apr 11:59 PM |
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| Submit your Assessment |
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| NOTE: Only team leaders can make the final submission. |
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| FAQs |
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| Frequently Asked Questions |
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| Need help? Reach out to us |
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| help_openenvhackathon@scaler.com |
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| Contact Support |
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| submission Deadline: 8th April 11:59 PM |
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| Submit your Assessment |
| → |
| How to Submit? |
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