context_scaler.txt

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Registration

14th March - 3rd April

Declaration

Before R1

Prepare

Now - 25th March

Round 1

25th March - 8th April

Results

10th April

Finale

25th-26th April

Welcome Roshni Nadella!

roshninadella@gmail.com
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Preparatory Course

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step 1

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Sravya Duvvuri
sravyad13@gmail.com
Team Lead
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Roshni Nadella
roshninadella@gmail.com
You
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Team is finalized. You’re all set — no further action needed.

OpenEnv Round 1 Bootcamp

OpenEnv Round 1 Bootcamp

OpenEnv Round 1 Bootcamp

OpenEnv Round 1 Bootcamp

OpenEnv Round 1 Bootcamp

OpenEnv Round 1 Bootcamp

OpenEnv Round 1 Bootcamp

OpenEnv Round 1 Bootcamp

OpenEnv Round 1 Bootcamp

OpenEnv Round 1 Bootcamp

OpenEnv Round 1 Bootcamp

OpenEnv Round 1 Bootcamp

OpenEnv Round 1 Bootcamp: Build Your First RL Environment

Live walkthrough to submit a strong Round 1 entry

timing

8:00 PM Onwards

Wednesday, 1st April

Host


Ben Burtenshaw

Community Education in AI at Hugging Face


Pulkit Aneja

Scaler Instructor

Watch Recording

PROBLEM STATEMENT

Round 1 — Problem Statement

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

NEW
Sample Inference Script 

"""
Inference Script Example
===================================
MANDATORY
- 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.
    LOCAL_IMAGE_NAME The name of the local image to use for the environment if you are using from_docker_image()
                     method

- Defaults are set only for API_BASE_URL and MODEL_NAME 
    (and should reflect your active inference setup):
    API_BASE_URL = os.getenv("API_BASE_URL", "<your-active-endpoint>")
    MODEL_NAME = os.getenv("MODEL_NAME", "<your-active-model>")
    
- 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

STDOUT FORMAT
- The script must emit exactly three line types to stdout, in this order:

    [START] task=<task_name> env=<benchmark> model=<model_name>
    [STEP]  step=<n> action=<action_str> reward=<0.00> done=<true|false> error=<msg|null>
    [END]   success=<true|false> steps=<n> score=<score> rewards=<r1,r2,...,rn>

  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]

  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
"""

import asyncio
import os
import textwrap
from typing import List, Optional

from openai import OpenAI

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")

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")
    asyncio.run(main())
NEW
Pre Validation Script

#!/usr/bin/env bash
  stop_at "Step 2"
fi

if [ -f "$REPO_DIR/Dockerfile" ]; then
  DOCKER_CONTEXT="$REPO_DIR"
elif [ -f "$REPO_DIR/server/Dockerfile" ]; then
  DOCKER_CONTEXT="$REPO_DIR/server"
else
  fail "No Dockerfile found in repo root or server/ directory"
  stop_at "Step 2"
fi

log "  Found Dockerfile in $DOCKER_CONTEXT"

BUILD_OK=false
BUILD_OUTPUT=$(run_with_timeout "$DOCKER_BUILD_TIMEOUT" docker build "$DOCKER_CONTEXT" 2>&1) && BUILD_OK=true

if [ "$BUILD_OK" = true ]; then
  pass "Docker build succeeded"
else
  fail "Docker build failed (timeout=${DOCKER_BUILD_TIMEOUT}s)"
  printf "%s\n" "$BUILD_OUTPUT" | tail -20
  stop_at "Step 2"
fi

log "${BOLD}Step 3/3: Running openenv validate${NC} ..."

if ! command -v openenv &>/dev/null; then
  fail "openenv command not found"
  hint "Install it: pip install openenv-core"
  stop_at "Step 3"
fi

VALIDATE_OK=false
VALIDATE_OUTPUT=$(cd "$REPO_DIR" && openenv validate 2>&1) && VALIDATE_OK=true

if [ "$VALIDATE_OK" = true ]; then
  pass "openenv validate passed"
  [ -n "$VALIDATE_OUTPUT" ] && log "  $VALIDATE_OUTPUT"
else
  fail "openenv validate failed"
  printf "%s\n" "$VALIDATE_OUTPUT"
  stop_at "Step 3"
fi

printf "\n"
printf "${BOLD}========================================${NC}\n"
printf "${GREEN}${BOLD}  All 3/3 checks passed!${NC}\n"
printf "${GREEN}${BOLD}  Your submission is ready to submit.${NC}\n"
printf "${BOLD}========================================${NC}\n"
printf "\n"

exit 0
Submission window opens on 28th March

Deadline: 8 Apr 11:59 PM


Submit your Assessment
→
Study material

Preparatory Course

4 modules · ~3.5 hours 

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

 Module 1: Why OpenEnv?

ESSENTIAL FOR ROUND 1

45 min

Module 2: Using Existing Environments

ESSENTIAL FOR ROUND 1

50 min

 Module 3: Deploying Environments

ESSENTIAL FOR ROUND 1

45 min

Module 4: Building Your Own Environment

 MOST IMPORTANT FOR ROUND 1

60 min

View full course repository

GUIDE

Round 1 Guide

What to Expect

Prerequisites

How to Submit

When Round 1 opens, you'll choose 1 of 4–5 problem statements and build an OpenEnv environment around it.

Example of what a problem statement looks like

"Build a mini-game RL environment with clearly defined tasks, automated graders, and reward logic using the OpenEnv framework."

→ Create a mini-game an AI agent can play

→ Define tasks with increasing difficulty

→ Write graders that verify task completion

→ Define reward logic for scoring

→ Package using OpenEnv for automated evaluation

Evaluation Criteria

Runtime correctness

Runs without errors

Interface compliance

Follows OpenEnv standard

Task design

 Clear, realistic, testable

Grading logic

 Reward system makes sense

Step 2

Submit your Assessment

Complete Step 1 first

Problem Statement is live. Build and submit.

Round 1 begins 

Submission window opens on 28th March

Deadline: 8 Apr 11:59 PM


Submit your Assessment
→
NOTE: Only team leaders can make the final submission.

FAQs

Frequently Asked Questions