| use conda env "sclr" |
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| THE TASK |
| Build a complete, real-world OpenEnv environment that an AI agent can learn from through the standard step() / reset() / state() API. |
| KEY REQUIREMENTS AT A GLANCE |
| Must simulate a real-world task (not games or toys) |
| Implement full OpenEnv spec: typed models, step()/reset()/state(), openenv.yaml |
| Minimum 3 tasks with agent graders (easy → medium → hard, scores/reward 0.0–1.0) |
| Meaningful reward function with partial progress signals |
| Baseline inference script with reproducible scores |
| Deploy to Hugging Face Spaces + working Dockerfile |
| README with environment description, action/observation spaces, setup instructions |
| FUNCTIONAL REQUIREMENTS |
| Real-world task simulation |
| 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. |
| OpenEnv spec compliance |
| 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. |
| Minimum 3 tasks with agent graders |
| 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. |
| Meaningful reward function |
| 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). |
| Baseline inference script |
| 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. |
| Detailed Requirements |
| NON-FUNCTIONAL REQUIREMENTS |
| Deploys to a Hugging Face Space |
| Environment must run as a containerized HF Space tagged with openenv. |
| Containerized execution |
| Must include a working Dockerfile. The environment should start cleanly with docker build + docker run. |
| Documentation |
| README must include: environment description and motivation, action and observation space definitions, task descriptions with expected difficulty, setup and usage instructions, baseline scores. |
| PARAMETER |
| WEIGHT |
| DESCRIPTION |
| Real-world utility |
| 30% |
| Does the environment model a genuine task? Would someone actually use this to train or evaluate agents? |
| Task & grader quality |
| 25% |
| Are tasks well-defined with clear objectives? Do graders accurately and fairly measure success? Meaningful difficulty progression? |
| Environment design |
| 20% |
| Clean state management, sensible action/observation spaces, good reward shaping, proper episode boundaries. |
| Code quality & spec compliance |
| 15% |
| Follows OpenEnv spec, clean project structure, typed models, documented, tested, Dockerfile works. |
| Creativity & novelty |
| 10% |
| Novel problem domain, interesting mechanics, clever reward design, original approach. |
| SCORING BREAKDOWN |
| Real-world utility (30%) |
| • 0–5: Toy/artificial problem with no practical application |
| • 6–15: Valid domain but shallow modeling of the real task |
| • 16–25: Good domain modeling, would be useful for agent evaluation |
| • 26–30: Excellent — fills a real gap, immediate value for the RL/agent community |
| Task & grader quality (25%) |
| • 3+ tasks with difficulty range? |
| • Graders produce scores between 0.0–1.0? |
| • Graders deterministic and reproducible? |
| • Hard task genuinely challenges frontier models? |
| Environment design (20%) |
| • reset() produces clean state? |
| • Action/observation types well-designed and documented? |
| • Reward function provides useful varying signal (not just sparse)? |
| • Episode boundaries sensible? |
| Code quality & spec compliance (15%) |
| • openenv validate passes? |
| • docker build && docker run works? |
| • HF Space deploys and responds? |
| • Baseline script runs and reproduces scores? |
| Creativity & novelty (10%) |
| • Domain we haven’t seen in OpenEnv before? |
| • Reward design has interesting properties? |
| • Clever mechanics that make the environment engaging? |
| Evaluation Criteria |
| Phase 1: Automated Validation |
| Pass/fail gate — HF Space deploys, OpenEnv spec compliance, Dockerfile builds, baseline reproduces, 3+ tasks with graders. |
| Phase 2: Agentic Evaluation |
| Scored — baseline agent re-run, standard Open LLM agent (e.g. Nemotron 3 Super) run against all environments, score variance check. |
| Phase 3: Human Review |
| Top submissions reviewed by Meta and Hugging Face engineers for real-world utility, creativity, and exploit checks. |
| DISQUALIFICATION CRITERIA |
| Environment does not deploy or respond |
| Plagiarized or trivially modified existing environments |
| Graders that always return the same score |
| No baseline inference script |
| How Judging works |
| Pre-Submission Checklist — all must pass or you're disqualified |
| HF Space deploys |
| Automated ping to the 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 the submitted repo |
| Baseline reproduces |
| Run the submitted 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 |
| Mandatory Additional Instructions |
| Before submitting, ensure the following variables are defined in your environment configuration: |
| 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. |
| The inference script must be named `inference.py` and placed in the root directory of the project |
| Participants must use OpenAI Client for all LLM calls using above variables |
| 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. |
| Infra Restrictions |
| Runtime of inference script should be less than 20min |
| Make sure your env and inference can run on a machine with vcpu=2, memory=8gb |
| Validator |
| Run the pre-submission validation script before submitting |
