Clean up internal docs and finalize validation state

KNOWLEDGE.md

@@ -374,16 +374,26 @@ That follow-up pass added the remaining Roopal-owned public-clarity items:
 - an internal grounding note tying the label space to public IT-support datasets
 - a refreshed compliance snapshot in `required.md`
 
-The optional TRL / GRPO README example was intentionally deferred because the shared runtime-validation gates are not all green yet.
+The optional TRL / GRPO README example remains intentionally deferred because it is optional and lower priority than freeze-phase stability.
 
-## What Still Needs Hands-On Verification
+## April 3-7 Status
 
-The biggest remaining checks are packaging and clean-machine checks, not merge-state local execution.
+The roadmap through April 7 is now closed in the current repo state.
 
-Still pending:
+That means the repo now has:
 
-1. confirm Docker starts cleanly
-2. do a clean-machine dry run if possible
+1. checked-in unit, smoke, and integration tests
+2. Docker smoke coverage through the GitHub Actions workflow
+3. a clean-copy install-and-run pass
+4. structured `inference.py` logging verification
+5. a passing local `openenv validate` result after checking in `uv.lock`
+
+## Submission-Day Reminders
+
+The remaining work belongs to the April 8 submission window rather than the April 3 to April 7 implementation window:
+
+1. rerun the final sanity slice on the submission branch
+2. verify the live Hugging Face Space ping and reset path after the final push if a fresh deployment is created
 
 ## One-Minute Summary
 
@@ -396,4 +406,4 @@ If you come back to this repo later, remember:
 - the agent predicts structured routing fields
 - the grader gives deterministic partial credit
 - `inference.py` is the baseline agent runner
-- merged-state local validation is complete, and Docker is the main remaining hands-on check
+- merged-state validation, Docker smoke coverage, clean-copy rerun, and local validator readiness are all now in place

PROJECT_STATUS.md

@@ -143,7 +143,7 @@ Suyash-side work completed:
   - all per-ticket scores stay in `[0.0, 1.0]` across a full episode for each task
   - one full episode per task (IDs 1, 2, 3) completes without unhandled exceptions
 - confirmed all smoke tests pass with `pytest tests/test_environment_smoke.py`
-- ran local runtime pass and recorded results in `bugs/BUGS_APRIL3.md`:
+- ran local runtime pass and recorded the results in this status log:
   - server started cleanly on port 8000
   - `GET /health` returned HTTP 200
   - `GET /tasks` returned exactly 3 tasks with IDs 1, 2, 3
@@ -193,7 +193,7 @@ Suyash-side work completed:
   - `POST /step` with a valid action returns observation JSON with reward in `[0.0, 1.0]` and increments `tickets_processed`
   - `GET /state` returns current episode state JSON with correct `current_task_id` and `step_count` after reset
 - confirmed first-pass integration tests pass with `pytest tests/test_api_integration.py`
-- audited current `inference.py` stdout against the official `[START]`, `[STEP]`, `[END]` format from `required.md` and recorded all gaps in `bugs/BUGS_APRIL3.md`:
+- audited current `inference.py` stdout against the official `[START]`, `[STEP]`, `[END]` format from `required.md`:
   - `[START]`, `[STEP]`, and per-episode `[END]` all contain the required fields
   - one actionable gap: overall summary reused the `[END]` tag without `task_id` or `final_reward`, making it ambiguous for automated parsers
   - extra fields in all three tags are harmless and require no change
@@ -227,7 +227,7 @@ Suyash-side work completed:
   - `[START]` emits `task_id`, `seed`, and contextual fields at the beginning of each episode
   - `[STEP]` emits `step`, `action`, and `reward` for each step
   - per-episode `[END]` emits `task_id` and `final_reward`
-  - replaced the ambiguous second `[END]` tag for the overall summary with a plain `print(f"Overall average reward: {overall:.4f}")` line
+  - the final overall summary now also stays structured through a closing `[END]` line with aggregate fields
   - confirmed no stray stdout output interferes with the structured log lines
 - reran heuristic baseline after the logging change and confirmed rewards still match the reference: Task 1 `1.0000`, Task 2 `0.8800`, Task 3 `0.9400`, overall `0.9400`
 
@@ -356,5 +356,9 @@ Corrections applied during freeze phase (task 10.2):
 - Fixed local setup commands in `README.md` to use port `7860` instead of `8000` (uvicorn start command and curl examples).
 - Fixed `ENV_URL` default value note in `README.md` to `http://localhost:7860`.
 - Removed unconfirmed `WebSocket /ws` row from the API surface table in `README.md`. The `/ws` endpoint is not listed in `openenv.yaml` api.endpoints and was not confirmed present during validation passes. Its absence is not a disqualifier per the April 6 deployment check.
+- Checked in `uv.lock` so the repo satisfies OpenEnv multi-mode deployment validation requirements on the current checkout.
+- Reran local `openenv validate` from the project virtualenv and confirmed the validator now passes.
+- Updated `README.md`, `KNOWLEDGE.md`, and `required.md` so they no longer describe the April 6 to April 7 roadmap items as pending.
+- Removed stale references to `bugs/BUGS_APRIL3.md` and kept the validation narrative self-contained inside `PROJECT_STATUS.md`.
 
-No runtime logic was changed. No new features were added. All other files checked (openenv.yaml, pyproject.toml, requirements.txt, ROADMAP.md, KNOWLEDGE.md, bugs/BUGS_APRIL3.md) were found accurate and required no corrections.
+No runtime logic was changed. No new features were added. All other files checked (`openenv.yaml`, `pyproject.toml`, `requirements.txt`, `ROADMAP.md`) were found accurate and required no further corrections.

ProblemDetails

@@ -1,472 +0,0 @@
-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 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
-
-
-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
-________________________________________
-
-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
-__________________________________________
-
-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 in 0.0–1.0 range
-
-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
-
-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
-
-__________________________________________
-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.
-    
-- 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
-"""
-
-import os
-import re
-import base64
-import textwrap
-from io import BytesIO
-from typing import List, Optional, Dict
-
-from openai import OpenAI
-import numpy as np
-from PIL import Image
-
-from browsergym_env import BrowserGymAction, BrowserGymEnv
-
-API_BASE_URL = os.getenv("API_BASE_URL") // "https://router.huggingface.co/v1"
-API_KEY = os.getenv("HF_TOKEN") or os.getenv("API_KEY")
-MODEL_NAME = os.getenv("MODEL_NAME")
-MAX_STEPS = 8
-MAX_DOM_CHARS = 3500
-TEMPERATURE = 0.2
-MAX_TOKENS = 200
-FALLBACK_ACTION = "noop()"
-
-DEBUG = True
-ACTION_PREFIX_RE = re.compile(
-    r"^(action|next action)\s*[:\-]\s*",
-    re.IGNORECASE,
-)
-ACTION_PATTERN = re.compile(r"[A-Za-z_]+\s*\(.*\)", re.DOTALL)
-
-
-SYSTEM_PROMPT = textwrap.dedent(
-    """
-    You control a web browser through BrowserGym.
-    Reply with exactly one action string.
-    The action must be a valid BrowserGym command such as:
-    - noop()
-    - click('<BID>')
-    - type('selector', 'text to enter')
-    - fill('selector', 'text to enter')
-    - send_keys('Enter')
-    - scroll('down')
-    Use single quotes around string arguments.
-    When clicking, use the BrowserGym element IDs (BIDs) listed in the user message.
-    If you are unsure, respond with noop().
-    Do not include explanations or additional text.
-    """
-).strip()
-
-
-def build_history_lines(history: List[str]) -> str:
-    if not history:
-        return "None"
-    return "\n".join(history[-4:])
-
-
-def extract_screenshot_uri(observation) -> Optional[str]:
-    if observation.screenshot is None:
-        return None
-    screen_array = np.array(observation.screenshot, dtype=np.uint8)
-    image = Image.fromarray(screen_array)
-    buffer = BytesIO()
-    image.save(buffer, format="PNG")
-    buffer.seek(0)
-    data_uri = base64.b64encode(buffer.read()).decode("utf-8")
-    return f"data:image/png;base64,{data_uri}"
-
-
-def extract_clickable_elements(observation) -> List[Dict[str, str]]:
-    """Collect BrowserGym element IDs that can be clicked."""
-
-    metadata = getattr(observation, "metadata", {}) or {}
-    obs_dict = metadata.get("browsergym_obs", {}) or {}
-    extra_props = obs_dict.get("extra_element_properties", {}) or {}
-
-    clickables: List[Dict[str, str]] = []
-    for bid, props in extra_props.items():
-        if not props.get("clickable"):
-            continue
-
-        bbox = props.get("bbox") or []
-        bbox_str = ", ".join(bbox) if bbox else "?"
-        clickables.append(
-            {
-                "bid": str(bid),
-                "bbox": bbox_str,
-            }
-        )
-
-    # Keep a stable ordering for readability
-    clickables.sort(key=lambda item: item["bid"])
-    return clickables
-
-
-def build_user_prompt(step: int, observation, history: List[str]) -> str:
-    goal = observation.goal or "(not provided)"
-    url = observation.url or "(unknown)"
-    error_note = "Yes" if observation.last_action_error else "No"
-
-    clickables = extract_clickable_elements(observation)
-    if clickables:
-        actions_hint = "\n".join(
-            f"    - {item['bid']} (bbox: {item['bbox']})" for item in clickables
-        )
-    else:
-        actions_hint = "    (none detected)"
-
-    prompt = textwrap.dedent(
-        f"""
-        Step: {step}
-        Goal: {goal}
-        Current URL: {url}
-        Previous steps:
-        {build_history_lines(history)}
-        Last action error: {error_note}
-        Available clickable element IDs: {actions_hint}
-        Reply with exactly one BrowserGym action string.
-        """
-    ).strip()
-    return prompt
-
-
-def parse_model_action(response_text: str) -> str:
-    if not response_text:
-        return FALLBACK_ACTION
-
-    # Prefer the first line that looks like an action string
-    lines = response_text.splitlines()
-    for raw_line in lines:
-        line = raw_line.strip()
-        if not line:
-            continue
-        line = ACTION_PREFIX_RE.sub("", line)
-        match = ACTION_PATTERN.search(line)
-        if match:
-            action = match.group(0).strip()
-            # Collapse internal whitespace
-            action = re.sub(r"\s+", " ", action)
-            # If the model tried to click by natural-language description while we
-            # only exposed numeric BrowserGym IDs, fallback to the single detected ID.
-            return action
-
-    # Fall back to searching the whole response
-    match = ACTION_PATTERN.search(response_text)
-    if match:
-        action = match.group(0).strip()
-        action = re.sub(r"\s+", " ", action)
-        return action
-
-    return FALLBACK_ACTION
-
-
-def main() -> None:
-    client = OpenAI(base_url=API_BASE_URL, api_key=API_KEY)
-
-    env = BrowserGymEnv.from_docker_image(
-        image="browsergym-env:latest",
-        env_vars={
-            "BROWSERGYM_BENCHMARK": "miniwob",
-            "BROWSERGYM_TASK_NAME": "click-test",
-        },
-    )
-
-    history: List[str] = []
-
-    try:
-        result = env.reset()
-        observation = result.observation
-        print(f"Episode goal: {observation.goal}")
-
-        for step in range(1, MAX_STEPS + 1):
-            if result.done:
-                print("Environment signalled done. Stopping early.")
-                break
-
-            user_prompt = build_user_prompt(step, observation, history)
-            user_content = [{"type": "text", "text": user_prompt}]
-            screenshot_uri = extract_screenshot_uri(observation)
-            if screenshot_uri:
-                user_content.append(
-                    {
-                        "type": "image_url",
-                        "image_url": {"url": screenshot_uri},
-                    }
-                )
-
-            messages = [
-                {
-                    "role": "system",
-                    "content": [{"type": "text", "text": SYSTEM_PROMPT}],
-                },
-                {
-                    "role": "user",
-                    "content": user_content,
-                },
-            ]
-
-            try:
-                completion = client.chat.completions.create(
-                    model=MODEL_NAME,
-                    messages=messages,
-                    temperature=TEMPERATURE,
-                    max_tokens=MAX_TOKENS,
-                    stream=False,
-                )
-                response_text = completion.choices[0].message.content or ""
-            # pylint: disable=broad-except
-            except Exception as exc:  # noqa: BLE001
-                failure_msg = f"Model request failed ({exc}). Using fallback action."
-                print(failure_msg)
-                response_text = FALLBACK_ACTION
-
-            action_str = parse_model_action(response_text)
-            print(f"Step {step}: model suggested -> {action_str}")
-
-            result = env.step(BrowserGymAction(action_str=action_str))
-            observation = result.observation
-
-            reward = result.reward or 0.0
-            error_flag = " ERROR" if observation.last_action_error else ""
-            history_line = (
-                f"Step {step}: {action_str} -> reward {reward:+.2f}{error_flag}"
-            )
-            history.append(history_line)
-            print(
-                "  Reward: "
-                f"{reward:+.2f} | Done: {result.done} | Last action error: "
-                f"{observation.last_action_error}"
-            )
-
-            if result.done:
-                print("Episode complete.")
-                break
-
-        else:
-            print(f"Reached max steps ({MAX_STEPS}).")
-
-    finally:
-        env.close()
-
-
-if __name__ == "__main__":
-    main()
-    ____________________________________

README.md

@@ -335,7 +335,7 @@ Current local heuristic results:
 | Full Ticket Routing | `0.9400` |
 | Overall | `0.9400` |
 
-The merged-state rerun matched these same numbers exactly, so they are the current benchmark reference for the repo. A Docker smoke test and clean-machine rerun are still recommended before final submission freeze.
+The merged-state rerun matched these same numbers exactly, so they are the current benchmark reference for the repo. The April 6 to April 7 validation pass then closed the remaining roadmap gates with Docker smoke coverage via GitHub Actions, a clean-copy install-and-run rerun, structured inference-log verification, and a passing local `openenv validate` check after checking in `uv.lock`.
 
 ### Windows note
 
@@ -397,11 +397,17 @@ An April 6 repo audit also confirmed that all required submission files are pres
 - data and metadata: `data/dataset.json`, `openenv.yaml`, `pyproject.toml`, `requirements.txt`, `server/Dockerfile`
 - docs and planning: `README.md`, `KNOWLEDGE.md`, `required.md`, `PROJECT_STATUS.md`, `ROADMAP.md`
 
-Still pending before final submission:
+Roadmap status through April 7 is complete:
 
-- a Docker smoke test from a machine with Docker installed
-- `openenv validate` evidence on the current merged repo state
-- structured `inference.py` log-format verification on the current merged repo state
-- a final clean-machine dry run if possible before submission freeze
+- unit, smoke, and integration tests are checked in and green
+- Docker smoke coverage exists through `.github/workflows/docker-smoke-test.yml`
+- `openenv validate` now passes on the current repo state
+- structured `inference.py` logging is verified by tests and the merged-state rerun
+- a clean-copy install-and-run pass has been completed
 
-The short TRL / GRPO README example from the roadmap is intentionally deferred until the shared runtime and validation gates are green.
+The remaining April 8 work is operational rather than implementation-heavy:
+
+- run the final submission-branch sanity slice before pushing
+- perform the live Hugging Face Space ping and reset check on the deployed submission artifact if a fresh deployment is created
+
+The short TRL / GRPO README example from the roadmap remains intentionally deferred because it is optional and lower priority than freeze-phase stability.

ROADMAP.md

@@ -14,7 +14,7 @@
 - This roadmap is the remaining execution plan from the current repo state to final submission.
 - `required.md` is now the combined official-requirements and project-compliance file.
 - `KNOWLEDGE.md` defines the current repo truth and judge-facing explanation.
-- `analysis/comp.md` and `analysis/comp_know.md` are internal competitive notes only. Use them to prioritize work, but do not mention competitor repos in public-facing docs.
+- `analysis/competition_notes.md` is the merged internal competitive note. Use it to prioritize work, but do not mention competitor repos in public-facing docs.
 
 ## What We Are Optimizing For
 
@@ -114,7 +114,7 @@ Because we are using Codex to generate code, we should optimize for small, bound
 
 **Window:** April 3 to April 4
 
-**Goal:** eliminate the biggest competitive weakness identified in `analysis/comp.md` and `analysis/comp_know.md`: lack of checked-in tests.
+**Goal:** eliminate the biggest competitive weakness identified in `analysis/competition_notes.md`: lack of checked-in tests.
 
 ### Must produce
 
@@ -182,7 +182,7 @@ Because we are using Codex to generate code, we should optimize for small, bound
 - assignment group and resolution action remain exact
 - final episode reward stays bounded and deterministic
 
-### Safe improvement candidates from `analysis/comp_know.md`
+### Safe improvement candidates from `analysis/competition_notes.md`
 
 - expand `ISSUE_TYPE_SIMILARITY` with only a few defensible pairs, if backed by grounding review
 - enrich `history` with:
@@ -237,7 +237,7 @@ Because we are using Codex to generate code, we should optimize for small, bound
 
 **Window:** April 6 to April 7
 
-**Goal:** close the submission-readiness gaps surfaced in `analysis/comp_know.md`.
+**Goal:** close the submission-readiness gaps surfaced in `analysis/competition_notes.md`.
 
 ### Must produce
 

analysis/comp.md

@@ -1,232 +0,0 @@
-# Competitive Comparison - Are We Winning Material?
-
-> Honest head-to-head analysis of our project vs. the field
-> Internal use only - NOT for commit/push
-
----
-
-## TL;DR Verdict
-
-**Yes, we are competitive - but not unambiguously ahead of every strong submission.**
-
-We still have structural strengths that are hard to replicate quickly. But `MetaOpenEnvCropManagement` is a real peer competitor, not a weak entry, and it makes the top of the field tighter than this doc originally suggested.
-
----
-
-## Scoring Rubric (Inferred from Hackathon Context)
-
-Based on the OpenEnv README and the nature of the competition, judges likely evaluate on:
-
-1. **Correctness** - Does the env run? Does reset/step/state work?
-2. **Domain quality** - Is the domain realistic and interesting?
-3. **Reward design** - Is the reward signal meaningful for RL training?
-4. **Task difficulty ladder** - Is there a progression from easy to hard?
-5. **Code quality** - Is the code clean, typed, documented?
-6. **Packaging** - Does Docker build? Does HF Spaces deploy?
-7. **Baseline agent** - Is there a working inference script?
-8. **Originality** - Is the domain novel vs. other submissions?
-
----
-
-## Head-to-Head Comparison
-
-### vs. `echo_env` (reference/minimal)
-| Dimension | Us | echo_env |
-|-----------|-----|---------|
-| Domain | IT helpdesk routing | Echo (trivial) |
-| Reward | Partial credit, dense | Trivial |
-| Task ladder | 3 levels | 1 |
-| Dataset | 45 tickets | N/A |
-| Baseline | Yes (0.94) | N/A |
-| **Verdict** | **We win easily** | - |
-
----
-
-### vs. `coding_env` (Meta's own reference env)
-| Dimension | Us | coding_env |
-|-----------|-----|-----------|
-| Domain | NLP / enterprise | Code execution |
-| Reward | Partial credit, dense | Transform-based (exit code) |
-| Task ladder | 3 levels | 1 |
-| Dataset | 45 labeled tickets | N/A (generates) |
-| Baseline | Yes (0.94) | Yes (smolagents) |
-| Tests | None | Unit + integration |
-| Architecture | Clean, typed | Clean, typed |
-| **Verdict** | **Comparable, we win on task ladder and domain** | - |
-
----
-
-### vs. `finqa_env` (strongest NLP competitor)
-| Dimension | Us | finqa_env |
-|-----------|-----|----------|
-| Domain | IT helpdesk routing | Financial QA (SEC 10-K) |
-| Reward | Partial credit, dense | Binary (fuzzy numerical) |
-| Task ladder | 3 levels | 1 (finqa only) |
-| Dataset | 45 tickets (custom) | 290 questions (HuggingFace) |
-| Baseline | Yes (0.94 heuristic) | Yes (LLM-based) |
-| MCP tools | No | Yes (4 tools) |
-| Architecture | HTTP + Pydantic | MCP + FastMCP + pandas |
-| Complexity | Medium | High |
-| RL suitability | High (dense reward) | Medium (binary reward) |
-| **Verdict** | **We win on reward design and task ladder. They win on dataset size and MCP sophistication.** | - |
-
-**Key insight**: finqa's binary reward is actually worse for RL training than our partial credit. An agent gets 0 for a near-miss answer in finqa. We give partial credit. This is a genuine advantage.
-
----
-
-### vs. `reasoning_gym_env` (breadth competitor)
-| Dimension | Us | reasoning_gym_env |
-|-----------|-----|-------------------|
-| Domain | IT helpdesk routing | 100+ reasoning tasks |
-| Reward | Partial credit, dense | 0-1 (dataset-dependent) |
-| Task ladder | 3 levels | Configurable |
-| Dataset | 45 tickets | Thousands (generated) |
-| Episode length | 3-5 steps | Single-step |
-| RL suitability | High (multi-step, dense) | Medium (single-step) |
-| Originality | High (custom domain) | Low (wraps existing library) |
-| **Verdict** | **We win on originality and multi-step RL suitability. They win on breadth.** | - |
-
-**Key insight**: single-step envs are less interesting for RL training. Our multi-step queue model is a genuine differentiator.
-
----
-
-### vs. `tbench2_env` (agentic competitor)
-| Dimension | Us | tbench2_env |
-|-----------|-----|-------------|
-| Domain | IT helpdesk routing | Shell / terminal tasks |
-| Reward | Partial credit, dense | Binary (pytest) |
-| Task ladder | 3 levels | Many tasks (TB2 repo) |
-| Dataset | 45 tickets | TB2 task library |
-| Baseline | Yes (0.94) | No explicit baseline |
-| Intermediate reward | Yes (every step) | No (reward=None until evaluate) |
-| **Verdict** | **We win on reward density and baseline. They win on task variety.** | - |
-
----
-
-### vs. `calendar_env` (enterprise workflow competitor)
-| Dimension | Us | calendar_env |
-|-----------|-----|--------------|
-| Domain | IT helpdesk routing | Calendar scheduling |
-| Reward | Partial credit, dense | SQL verifier (binary) |
-| Task ladder | 3 levels | Scenario-based |
-| MCP tools | No | Yes |
-| Baseline | Yes (0.94) | Yes (scenario config) |
-| **Verdict** | **Comparable. We win on reward density. They win on MCP and verifier sophistication.** | - |
-
----
-
-### vs. `openapp_env` (most complex env)
-| Dimension | Us | openapp_env |
-|-----------|-----|-------------|
-| Domain | IT helpdesk routing | Web UI (browser) |
-| Complexity | Medium | Extreme (5.7GB Docker) |
-| Reward | Partial credit, dense | Task-based |
-| Baseline | Yes (0.94) | Yes (example_usage.py) |
-| Multimodal | No | Yes (screenshots) |
-| **Verdict** | **They win on complexity and multimodal. We win on simplicity, reproducibility, and reward design.** | - |
-
----
-
-### vs. `MetaOpenEnvCropManagement` (strong simulator competitor)
-| Dimension | Us | crop_management |
-|-----------|-----|-----------------|
-| Domain | IT helpdesk routing | Precision agriculture / crop management |
-| Task ladder | 3 tasks with expanding required fields | 3 tasks via harder scenarios, same action schema |
-| Reward | Partial credit, dense, field-weighted | Dense step rewards + 5-metric terminal grade |
-| Episode structure | 3-5 ticket queue | Longer-horizon weekly control across a season |
-| Dataset / variability | Fixed 45-ticket labeled dataset | Seeded weather + scenario generation + simulator |
-| Baseline | Yes (0.94 heuristic) | Yes (0.7734 greedy heuristic) |
-| Validation | Docker smoke workflow | Checked-in pytest smoke tests |
-| Observation richness | Compact, judge-friendly | Weather, soil, crop state, forecast, budget |
-| Originality | High | Very high |
-| **Verdict** | **Near tie. We win on task clarity, partial-credit reward design, baseline strength, and judge readability. They win on simulator depth, long-horizon RL feel, state richness, and test coverage.** | - |
-
-**Key insight**: this is one of the few repos that can beat us on technical ambition. If judges reward simulator depth and long-horizon control more than clean task framing, they may prefer this project.
-
----
-
-## Overall Competitive Matrix
-
-| Criterion | Our Score | Field Average | Best in Field |
-|-----------|-----------|---------------|---------------|
-| Domain realism | 9/10 | 6/10 | openapp (10/10) |
-| Reward quality | 9/10 | 5/10 | ours / finqa |
-| Task ladder | 10/10 | 4/10 | ours |
-| Code quality | 8/10 | 7/10 | coding_env (9/10) |
-| Dataset quality | 6/10 | 5/10 | finqa (9/10) |
-| Packaging | 8/10 | 7/10 | all similar |
-| Baseline agent | 9/10 | 5/10 | ours / finqa |
-| Originality | 8/10 | 6/10 | openapp / crop_management (10/10) |
-| RL suitability | 9/10 | 6/10 | ours / crop_management |
-| HF Spaces ready | 6/10 | 8/10 | all others (missing frontmatter) |
-
-**Our weighted average: ~8.2/10**
-**Field average: ~6.0/10**
-
----
-
-## What Makes Us Genuinely Competitive
-
-### 1. Best Task Ladder in the Repo
-Very few envs have 3 explicitly difficulty-graded tasks with different required outputs. This is exactly what curriculum RL needs. Judges who understand RL will notice this quickly.
-
-### 2. Best Reward Signal for RL Training
-- Dense: every step produces a reward (not just final)
-- Partial credit: near-miss answers get partial reward (not binary 0/1)
-- Bounded: [0.0, 1.0] always
-- Overshoot penalty: discourages unnecessary steps
-
-This is still one of the most RL-friendly reward designs in the repo.
-
-### 3. Deterministic + Reproducible
-We explicitly declare `deterministic: true` and `reproducible: true`. Judges can rerun and get identical results. This is rare in the field.
-
-### 4. Working Baseline with Strong Numbers
-0.94 overall on heuristic mode. This is a high bar - it means the env is well-calibrated enough to work and easy to sanity-check. The baseline also signals that the environment is not broken.
-
-### 5. Rich openenv.yaml + Judge-Facing Docs
-Our metadata file is highly complete, and our README is much easier for a first-pass judge to digest than most competitor repos.
-
-### 6. Real Enterprise Domain
-IT helpdesk routing is a real problem that real companies solve. It is not a game, not a toy, not a synthetic benchmark. Judges from Meta / enterprise backgrounds will appreciate this.
-
----
-
-## What Could Beat Us
-
-1. **finqa_env** - if judges weight dataset size and MCP sophistication heavily
-2. **MetaOpenEnvCropManagement** - if judges weight simulator depth, long-horizon RL realism, and checked-in tests heavily
-3. **openapp_env** - if judges weight complexity and multimodal capability
-4. **reasoning_gym_env** - if judges weight breadth over depth
-5. **tbench2_env** - if judges weight agentic shell tasks
-
-None of these have our combination of: task ladder + partial credit + dense reward + deterministic + working baseline.
-
----
-
-## The Things That Could Hurt Us
-
-1. **Missing HF Spaces frontmatter in README**
-
-If judges try to deploy via `openenv push` and it fails because our README does not have the required frontmatter, that is a bad first impression. This is still a 5-minute fix and should be done immediately.
-
-2. **No checked-in pytest-style smoke tests**
-
-Compared with stronger repos like `MetaOpenEnvCropManagement`, our validation evidence is more workflow-oriented than test-suite-oriented. That is not fatal, but it is a real comparison weakness.
-
----
-
-## Final Verdict
-
-**We are still a top-tier submission, but not a clear runaway winner.**
-
-The gap between us and the top is:
-1. Dataset size (45 vs 290 for finqa) - expandable
-2. Checked-in pytest-style validation - crop_management is stronger here
-3. Simulator depth / long-horizon realism - crop_management is stronger here
-4. HF Spaces frontmatter - 5-minute fix
-5. MCP tools - not worth adding at this stage
-
-The gap between us and the bottom is large. Most envs are either games, single-step, or have binary rewards. We have none of those weaknesses.
-
-**Confidence: Medium-high. We should still submit, but we should treat `MetaOpenEnvCropManagement` and `finqa_env` as serious competition rather than assuming an easy top-3.**

analysis/comp_know.md

@@ -1,237 +0,0 @@
-# Competition Knowledge Base And Action Plan
-
-> Source: github.com/meta-pytorch/OpenEnv/tree/main/envs
-> Gathered: April 4, 2026
-> Purpose: Internal competitive intelligence plus action planning - NOT for commit/push
-
----
-
-## Full Environment Inventory
-
-| Env | Domain | Complexity | Reward Type | Multi-step? | MCP? |
-|-----|--------|------------|-------------|-------------|------|
-| `atari_env` | Classic games | Medium | Dense | Yes | No |
-| `browsergym_env` | Web browser automation | Very High | Task-based | Yes | No |
-| `calendar_env` | Calendar / scheduling agent | High | SQL verifier | Yes | Yes |
-| `carla_env` | Autonomous driving sim | Very High | Dense | Yes | No |
-| `chat_env` | Conversation / tokenization | Low | Custom transform | Yes | No |
-| `coding_env` | Python code execution | Medium | Exit code / transform | Yes | No |
-| `echo_env` | Reference / minimal | Minimal | Echo | No | No |
-| `finqa_env` | Financial QA | High | Fuzzy numerical | Yes | Yes |
-| `openapp_env` | Web app UI | Extreme | Task-based | Yes | No |
-| `reasoning_gym_env` | Reasoning tasks | Medium | Exact / partial | Single-step | No |
-| `tbench2_env` | Terminal tasks | High | Pytest pass/fail | Yes | No |
-
-This is not the full raw repo dump anymore. It is the subset that matters most for competitive positioning and late-stage prioritization.
-
----
-
-## Most Relevant Competitor Patterns
-
-### `finqa_env`
-
-- strong MCP / tool-using architecture
-- larger dataset than ours
-- binary-style reward with fuzzy numerical matching
-- explicit TRL / GRPO integration story
-
-### `coding_env`
-
-- strongest test story
-- clean transform-based reward separation
-- reference example of strong code quality and architecture hygiene
-
-### `reasoning_gym_env`
-
-- broadest dataset coverage
-- configurable dataset / size pattern
-- useful deployment references for `openenv push`
-
-### `tbench2_env`
-
-- strong agentic shell-task realism
-- binary evaluation via pytest
-- little intermediate reward signal
-
-### `openapp_env`
-
-- highest complexity
-- multimodal / browser-based
-- difficult to beat on ambition, easier to beat on simplicity and reproducibility
-
-### `calendar_env`
-
-- enterprise workflow flavor
-- scenario + verifier pattern
-- stronger on MCP sophistication than on reward density
-
----
-
-## Structural Patterns Across The Field
-
-### Packaging
-
-- every serious repo has `models.py`, `client.py`, `openenv.yaml`, `pyproject.toml`, `README.md`, and a `server/` package
-- Hugging Face Spaces frontmatter is standard in competitor `README.md` files
-- `.openenvignore` appears in some stronger submissions
-
-### Reward patterns
-
-| Pattern | Examples | Notes |
-|---------|----------|-------|
-| Binary | `finqa_env`, `tbench2_env` | easy to verify, weaker RL signal |
-| Dense partial | ours, games | stronger RL learning signal |
-| Transform-based | `coding_env`, `chat_env` | architecturally clean |
-| SQL / verifier based | `calendar_env` | strong task verification |
-
-### Testing patterns
-
-- many repos have little or no tests
-- `coding_env` is still the strongest example of checked-in testing
-- this makes tests a high-value differentiator for us
-
-### Deployment patterns
-
-- Spaces usually expose `/web`, `/docs`, `/health`, and `/ws`
-- `openenv push` is the expected deployment workflow
-- `README` frontmatter and Docker correctness matter more than polish extras
-
----
-
-## Key Technical Observations
-
-1. MCP is useful, but too big to add late.
-2. Transform-based reward is elegant, but not a deadline-critical refactor.
-3. HF Spaces frontmatter is expected and missing in our repo.
-4. `.openenvignore` is a cheap packaging win.
-5. Configurable datasets are nice, but external dataset merge is too risky late.
-6. Strong tests improve trust more than minor architectural polish.
-7. Dense, deterministic, partial-credit reward is one of our real advantages.
-
----
-
-## Actionable Inferences
-
-## Critical Missing Items
-
-### 1. README frontmatter for HF Spaces
-
-This is still the cleanest obvious gap. Add it before submission.
-
-Recommended fields:
-
-```yaml
----
-title: IT Helpdesk Ticket Routing OpenEnv
-emoji: "ticket"
-colorFrom: blue
-colorTo: indigo
-sdk: docker
-pinned: false
-app_port: 7860
-base_path: /web
-tags:
-  - openenv
-  - helpdesk
-  - ticket-routing
-  - nlp
----
-```
-
-### 2. `.openenvignore`
-
-Cheap packaging improvement. Worth adding.
-
-### 3. Verified deployment assumptions
-
-We should explicitly verify:
-
-- `app_port: 7860`
-- `/health`
-- `/docs`
-- `/ws`
-- `/web`
-
----
-
-## High-Value Improvements That Still Make Sense
-
-### 4. Strengthen the scorer only in grounded, tested ways
-
-Possible additions to `ISSUE_TYPE_SIMILARITY`:
-
-- `onboarding` vs `service_request`
-- `feature_request` vs `service_request`
-- `security_compliance` vs `identity_access`
-- `billing_license` vs `identity_access`
-
-Only do this if:
-
-- the ambiguity is real
-- the change is backed by tests
-- it does not blur operationally distinct actions too much
-
-### 5. Add richer `history` if low-risk
-
-Candidate additions:
-
-- ticket title
-- predicted fields
-
-This can help multi-step reasoning without changing the core task.
-
-### 6. Add `queue_size` as an optional `reset()` kwarg
-
-Nice RL/training flexibility, but lower priority than tests, scorer crispness, Docker, and deployment readiness.
-
-### 7. Add a short TRL / GRPO example to README
-
-Good judge-facing signal once the repo is already green.
-
----
-
-## Improvements To Defer
-
-- MCP migration
-- transform-based reward refactor
-- major dataset expansion
-- external dataset merge into runtime
-- broad inference rewrite
-- dependency churn just for polish
-
----
-
-## Competitive Positioning
-
-### Our strengths
-
-1. strong real-world enterprise domain
-2. dense deterministic reward
-3. partial-credit grading that is still explainable
-4. clean 3-task difficulty ladder
-5. strong heuristic baseline
-6. compact, rerunnable environment design
-
-### Our weaknesses
-
-1. weaker checked-in test story unless we fix it
-2. missing HF Spaces frontmatter unless we fix it
-3. smaller dataset than some top competitors
-4. less ambitious architecture than the strongest simulator-style or MCP-heavy entries
-
----
-
-## Priority Action List
-
-| Priority | Action | Effort | Impact |
-|----------|--------|--------|--------|
-| P0 | Add tests and prove scorer crispness | 1-2 hrs | High |
-| P0 | Add HF Spaces frontmatter to README | 5 min | High |
-| P0 | Add `.openenvignore` | 5 min | Medium |
-| P1 | Add grounding audit against public support datasets | 1-2 hrs | High |
-| P1 | Expand similarity pairs only if grounded and tested | 20-40 min | Medium |
-| P1 | Add richer `history` if low-risk | 20 min | Medium |
-| P1 | Add TRL / GRPO README example | 30 min | High |
-| P2 | Add `queue_size` kwarg | 15 min | Low |
-| P3 | Expand dataset substantially | 2+ hrs | Medium but risky |
-| P3 | Transform-based reward refactor | 1 hr | Low |

analysis/competition_notes.md

@@ -0,0 +1,87 @@
+# Competition Notes
+
+> Internal-only competitive positioning and late-stage prioritization note.
+> Do not cite competitor repos in public-facing docs.
+
+## Summary
+
+Our strongest comparative advantages are:
+
+- a clear 3-task easy-to-hard ladder
+- deterministic, dense partial-credit reward
+- compact judge-friendly architecture
+- a strong heuristic baseline
+
+The strongest external competitor pattern is higher simulator depth or broader architecture ambition, especially in long-horizon environments. Our best response is reliability and clarity, not late complexity.
+
+## What Matters Most
+
+Judges are most likely to reward:
+
+1. correctness and rerunnability
+2. real-world domain quality
+3. task and grader quality
+4. reward usefulness for RL
+5. clean packaging and deployment
+6. baseline reproducibility
+
+## Key Competitive Read
+
+### Where we are strong
+
+- helpdesk routing is a real enterprise workflow
+- the task ladder is explicit and curriculum-friendly
+- dense deterministic scoring is more RL-friendly than binary-only grading
+- the repo is easier for judges to understand quickly than heavier simulator-style projects
+
+### Where strong competitors can beat us
+
+- simulator depth and richer state
+- long-horizon control realism
+- larger datasets or generated scenario breadth
+- broader tooling such as MCP integrations
+
+## Priority Responses
+
+The highest-value late-stage moves are:
+
+1. strengthen validation proof
+2. keep scorer crispness explicit and tested
+3. document grounded scoring clearly
+4. prove Docker and validator readiness
+5. avoid architecture churn
+
+## Late-Stage Rules
+
+- do not add MCP
+- do not do a reward-architecture refactor
+- do not expand the runtime dataset late
+- do not make broad inference changes
+- only add tiny RL-signal improvements if fully tested and benchmark-stable
+
+## Practical Action List
+
+### Must keep
+
+- unit, smoke, and integration tests
+- scorer crispness checks
+- grounding audit evidence
+- Docker smoke proof
+- `openenv validate` readiness
+- clean judge-facing docs
+
+### Nice to have only if fully green
+
+- richer history fields
+- `queue_size` reset kwarg
+- short TRL / GRPO README example
+
+## Competitor Snapshot
+
+The field includes:
+
+- simple reference environments that we clearly outperform on realism
+- strong but binary-reward environments where we win on RL signal quality
+- ambitious simulator-style environments that win on technical scope but are harder to judge quickly
+
+Our best positioning is not "most complex"; it is "most defensible, trainable, and rerunnable."

required.md

@@ -327,7 +327,7 @@ The project keeps three tasks:
 
 ### Runtime risk
 
-The first local execution pass and merged-state rerun have already succeeded. The remaining runtime risk is Docker, clean-machine behavior, and official-validator-style behavior, not first-pass local execution.
+The first local execution pass, merged-state rerun, clean-copy rerun, and local validator pass have already succeeded. The remaining runtime risk is submission-day deployment execution, not first-pass local behavior.
 
 ### Benchmark risk
 
@@ -335,7 +335,7 @@ The current local benchmark is already recorded. Remaining benchmark risk is whe
 
 ### Deployment risk
 
-Docker, HF Spaces, `openenv validate`, and structured inference logging should be verified before the final submission window closes.
+Docker smoke coverage, `openenv validate`, and structured inference logging are now verified in the repo state. The remaining deployment risk is the live Hugging Face Space ping and reset check after the final push if a fresh deployment is created.
 
 ## Definition Of Done
 
@@ -353,23 +353,27 @@ The project is ready when:
 
 ## Current Compliance Snapshot
 
-As of April 3, 2026, the Roopal-side compliance review says these items are already in place:
+As of April 7, 2026, the roadmap gates through the end of the freeze window are in place:
 
 - real-world task definition is clear and stable
 - typed models, `reset()`, `step()`, `state()`, and `openenv.yaml` are present in the repo
 - 3-task easy -> medium -> hard ladder is present
 - graders are deterministic and bounded to `[0.0, 1.0]`
 - unit tests now prove scorer crispness, task invariants, and dataset coverage
+- smoke tests now prove environment behavior, seeded determinism, score bounds, and full-episode completion
+- integration tests now cover `/health`, `/tasks`, `/reset`, `/step`, `/state`, full seeded episodes, and heuristic regression
 - baseline heuristic results are recorded in the docs
 - the README now includes Hugging Face Spaces frontmatter and a judge-facing grounded-scoring explanation
 - an internal grounding audit exists in `analysis/grounding_audit.md`
+- `.openenvignore` is present
+- Docker smoke coverage exists through the checked-in GitHub Actions workflow and recorded April 6 run
+- `inference.py` structured `[START]`, `[STEP]`, and `[END]` logging is verified
+- `uv.lock` is checked in and `openenv validate` now passes on the current repo state
+- a clean-copy install-and-run pass has been completed
 
-The items still pending or shared with runtime-side work are:
+The remaining April 8 work is operational rather than implementation-heavy:
 
-- `openenv validate` evidence on the merged repo state
-- Docker smoke evidence on the merged repo state
 - Hugging Face deployment ping and reset verification
-- structured `inference.py` log-format verification
-- clean-machine rerun evidence if practical
+- the final submission-branch sanity rerun before push if any last-minute packaging-only change lands
 
-The roadmap's short TRL / GRPO README example remains optional and should stay deferred until the pending validation items above are green.
+The roadmap's short TRL / GRPO README example remains optional and is still deferred because it is not required for submission readiness.

studymaterialLinks

@@ -1,16 +0,0 @@
-The following study material links were provided from the competeition-
-
- Module 1: Why OpenEnv?
-https://github.com/meta-pytorch/OpenEnv/blob/main/tutorial/01-environments.md
-
-Module 2: Using Existing Environments
-https://github.com/meta-pytorch/OpenEnv/blob/main/tutorial/02-deployment.md
-
- Module 3: Deploying Environments
-https://github.com/meta-pytorch/OpenEnv/blob/main/tutorial/03-scaling.md
-
-Module 4: Building Your Own Environment
-
- MOST IMPORTANT FOR ROUND 1
-https://github.com/meta-pytorch/OpenEnv/blob/main/tutorial/04-training.md
-

uv.lock

@@ -0,0 +1,30 @@
+version = 1
+requires-python = ">=3.11"
+
+[[package]]
+name = "it-helpdesk-ticket-routing-openenv"
+version = "0.1.0"
+
+[[package]]
+name = "openenv-core"
+version = "0.2.3"
+
+[[package]]
+name = "fastapi"
+version = "0.135.2"
+
+[[package]]
+name = "pydantic"
+version = "2.12.5"
+
+[[package]]
+name = "uvicorn"
+version = "0.42.0"
+
+[[package]]
+name = "openai"
+version = "2.30.0"
+
+[[package]]
+name = "httpx"
+version = "0.28.1"