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Dockerfile

@@ -24,12 +24,9 @@ RUN apt-get update && \
 ARG BUILD_MODE=in-repo
 ARG ENV_NAME=debugZero
 
-# Copy environment code (always at root of build context)
-COPY . /app/env
-
-# For in-repo builds, openenv is already vendored in the build context
-# For standalone builds, openenv will be installed via pyproject.toml
-WORKDIR /app/env
+# Install the server runtime dependencies separately so the image doesn't
+# pull the full training stack from the repo root project.
+COPY server/requirements.txt /app/server-requirements.txt
 
 # Ensure uv is available (for local builds where base image lacks it)
 RUN if ! command -v uv >/dev/null 2>&1; then \
@@ -37,22 +34,13 @@ RUN if ! command -v uv >/dev/null 2>&1; then \
         mv /root/.local/bin/uv /usr/local/bin/uv && \
         mv /root/.local/bin/uvx /usr/local/bin/uvx; \
     fi
-    
-# Install dependencies using uv sync
-# If uv.lock exists, use it; otherwise resolve on the fly
-RUN --mount=type=cache,target=/root/.cache/uv \
-    if [ -f uv.lock ]; then \
-        uv sync --frozen --no-install-project --no-editable; \
-    else \
-        uv sync --no-install-project --no-editable; \
-    fi
 
 RUN --mount=type=cache,target=/root/.cache/uv \
-    if [ -f uv.lock ]; then \
-        uv sync --frozen --no-editable; \
-    else \
-        uv sync --no-editable; \
-    fi
+    uv venv /app/.venv && \
+    uv pip install --python /app/.venv/bin/python -r /app/server-requirements.txt
+
+# Copy environment code after dependency installation to preserve the cache
+COPY . /app/env
 
 # Final runtime stage
 FROM ${BASE_IMAGE}
@@ -60,7 +48,7 @@ FROM ${BASE_IMAGE}
 WORKDIR /app
 
 # Copy the virtual environment from builder
-COPY --from=builder /app/env/.venv /app/.venv
+COPY --from=builder /app/.venv /app/.venv
 
 # Copy the environment code
 COPY --from=builder /app/env /app/env

README.md

@@ -1,393 +1,189 @@
----
-title: DebugZero
-emoji: 🧪
-colorFrom: blue
-colorTo: green
-sdk: docker
-app_port: 8000
-pinned: false
-base_path: /web
----
-
-# DebugZero Environment
-
-DebugZero is an OpenEnv environment for training code models through adversarial debugging self-play.
-
-One model plays two roles:
-
-1. **Proposer**: receives a clean Python function and submits a realistic buggy version.
-2. **Solver**: receives the buggy function and submits a repaired version.
-
-The environment executes the submitted code against tests in a constrained Python sandbox and returns structured OpenEnv observations. The training pipeline turns those observations into scalar rewards for GRPO/Unsloth training.
-
-The goal is to teach an LLM a debugging skill that static supervised examples do not capture well: generating plausible failures, diagnosing them, and repairing code based on executable feedback.
-
-## Submission Links
-
-- **Hugging Face Space**: add the final submitted Space URL here before the deadline.
-- **Training notebook**: [`notebooks/train_colab.ipynb`](notebooks/train_colab.ipynb)
-- **OpenEnv manifest**: [`openenv.yaml`](openenv.yaml)
-
-## Why This Environment Matters
-
-Most code training data shows finished solutions. DebugZero instead creates a loop where the model has to reason about failure:
-
-- What kind of bug would a real programmer accidentally introduce?
-- Does the mutated program still parse and run?
-- Does it fail tests for a meaningful reason?
-- Can the solver recover the original intended behavior?
-
-That makes the environment useful for training debugging, program repair, adversarial test thinking, and execution-grounded code reasoning.
-
-## OpenEnv Integration
-
-DebugZero uses the standard OpenEnv client/server pattern.
-
-The manifest is:
-
-```yaml
-spec_version: 1
-name: debugZero
-type: space
-runtime: fastapi
-app: server.app:app
-port: 8000
-workers: 4
-max_concurrent_envs: 100
-```
-
-The FastAPI app is created with OpenEnv's server helper in [`server/app.py`](server/app.py):
-
-```python
-app = create_app(
-    DebugzeroEnvironment,
-    DebugzeroAction,
-    DebugzeroObservation,
-    env_name="debugZero",
-    max_concurrent_envs=int(os.environ.get("MAX_CONCURRENT_ENVS", "100")),
-)
-```
-
-Clients should interact with the environment through [`DebugzeroEnv`](client.py), not by importing server internals. The client serializes `DebugzeroAction` objects, parses OpenEnv `StepResult` payloads, and exposes the normal `reset`, `step`, and `state` flow.
-
-## Episode Flow
-
-Each episode is a two-turn game over one seed function.
-
-### 1. Reset
-
-`reset()` creates a fresh `DebugzeroState`:
-
-- `episode_id`: new UUID
-- `step_count`: `0`
-- `seed_id`: currently `HumanEval/0`
-- `original_code`: clean HumanEval seed implementation
-- `current_code`: initially the same clean code
-- `role_turn`: `proposer`
-
-The reset observation tells the agent that the proposer acts first and provides the clean function.
-
-### 2. Proposer Step
-
-The proposer sends:
-
-```json
-{
-  "role": "proposer",
-  "code": "<complete mutated Python function>"
-}
-```
-
-The environment:
-
-1. Stores the submitted code as `current_code`.
-2. Runs it with the seed tests using `execute_code`.
-3. Returns an observation with:
-   - `role_next = "solver"`
-   - `tests_passed`
-   - `syntax_error`
-   - truncated `execution_result`
-   - `done = false`
-
-A good proposer submission is syntax-valid, safe to execute, close to the original code, and causes tests to fail.
+---
+title: DebugZero Environment Server
+emoji: 🧪
+colorFrom: blue
+colorTo: indigo
+sdk: docker
+pinned: false
+app_port: 8000
+base_path: /web
+tags:
+  - openenv
+  - debugging
+  - self-play
+---
+
+# DebugZero
+
+DebugZero is an OpenEnv self-play debugging environment where one language model plays two roles:
+
+1. The Proposer injects a small, realistic bug into clean Python code.
+2. The Solver repairs the bug using the sandbox feedback.
+
+The project is built for the OpenEnv hackathon themes most closely aligned with:
+
+- Theme #1: Multi-Agent Interactions
+- Theme #4: Self-Improvement
+
+The current codebase is not a toy demo. It has a deterministic seed bank, a verified bug bank, role-aware rewards, a GRPO training loop, and a live API smoke test that exercises the same environment path used for training.
+
+## What Lives Where
+
+| File | Role |
+| --- | --- |
+| [server/tasks.py](server/tasks.py) | Curated 6-task environment task bank with canonical solutions and tests |
+| [seed_bank.py](seed_bank.py) | Compatibility re-export for the server task bank |
+| [bug_bank.py](bug_bank.py) | Deterministic verified bug generation and train/eval holdout split |
+| [server/debugZero_environment.py](server/debugZero_environment.py) | OpenEnv environment state machine for proposer/solver turns |
+| [server/executor.py](server/executor.py) | Sandboxed subprocess execution for code plus tests |
+| [server/bug_injector.py](server/bug_injector.py) | AST mutation engine for realistic bug injection |
+| [server/graders.py](server/graders.py) | Role-aware reward shaping, solve-rate history, and AST-distance scoring |
+| [training/dual_role_sampler.py](training/dual_role_sampler.py) | Proposer and solver prompt templates |
+| [training/grpo_train.py](training/grpo_train.py) | Mixed-role GRPO dataset build, eval, and training workflow |
+| [eval/api_baseline.py](eval/api_baseline.py) | Deterministic controls plus live API promise-check harness |
+| [inference.py](inference.py) | Multi-episode inference runner with flat `[START]`/`[STEP]`/`[END]` logs |
+| [client.py](client.py) | OpenEnv client wrapper |
+| [models.py](models.py) | Shared action/observation/state models |
+| [notebooks/train_colab.ipynb](notebooks/train_colab.ipynb) | Notebook-first training workflow |
 
-### 3. Solver Step
+## How The Environment Works
 
-The solver sends:
+Each episode starts from a seed function drawn from the curated bank. The Proposer mutates the clean function. The environment executes the candidate code in a sandbox, runs the seed-specific tests, and returns:
 
-```json
-{
-  "role": "solver",
-  "code": "<complete repaired Python function>"
-}
-```
+- `tests_passed`
+- `syntax_error`
+- `execution_result`
+- `role_next`
+- `metadata` including `seed_id`, `original_code`, and bug context when present
 
-The environment:
+If the Proposer creates a real failing bug, the Solver gets that buggy code plus the failure summary and attempts to fix it. The current environment cycles deterministically through the seed bank so repeated runs are reproducible.
 
-1. Stores the submitted repair as `current_code`.
-2. Runs it against the same tests.
-3. Returns an observation with:
-   - `role_next = "end"`
-   - `tests_passed`
-   - `syntax_error`
-   - truncated `execution_result`
-   - `done = true`
+## Dataset
 
-A good solver submission passes tests without syntax errors.
+The current task bank is intentionally small and reproducible:
 
-## Action, Observation, and State Schemas
+- 6 curated seed tasks
+- 18 verified solver training bugs
+- 6 eval holdout bugs
+- 27 mixed-role rows per dataset build
 
-The OpenEnv models live in [`models.py`](models.py).
+The six seeds are:
 
-### Action
+- `has_close_elements`
+- `sum_to_n`
+- `middle_slice`
+- `is_non_decreasing`
+- `count_nonempty`
+- `running_max`
 
-`DebugzeroAction` extends OpenEnv `Action`:
+The bug bank is not random text. It is built at runtime by applying AST mutations and keeping only verified bugs that:
 
-| Field | Type | Meaning |
-| --- | --- | --- |
-| `role` | `str` | Either `proposer` or `solver`. |
-| `code` | `str` | The complete buggy or repaired Python function. |
+- change the code
+- still parse
+- pass safety checks
+- fail the seed tests
 
-### Observation
+The default bug operators are:
 
-`DebugzeroObservation` extends OpenEnv `Observation`:
+- `wrong_operator`
+- `wrong_builtin`
+- `condition_negation`
+- `off_by_one`
+- `loop_boundary_shift`
+- `slice_boundary_corruption`
 
-| Field | Type | Meaning |
-| --- | --- | --- |
-| `role_next` | `str` | Which role should act next. |
-| `current_code` | `str` | Current code after reset or step. |
-| `execution_result` | `str` | Captured stdout/stderr summary from sandbox execution. |
-| `tests_passed` | `bool` | Whether the submitted code passed the environment tests. |
-| `syntax_error` | `bool` | Whether parsing or execution produced a syntax error. |
-| `done` | `bool` | OpenEnv completion flag. |
-| `reward` | `float` | Server currently returns `0.0`; training code computes shaped rewards externally. |
+The noisier mutators `variable_swap` and `missing_base_case` are kept out of the default bank so the training signal stays clean. Train/eval splitting is deterministic, and the eval side keeps one harder holdout bug per seed.
 
-### State
+## Rewards
 
-`DebugzeroState` extends OpenEnv `State`:
+The reward design is role-aware and intentionally simple:
 
-| Field | Type | Meaning |
+| Role | State | Reward |
 | --- | --- | --- |
-| `seed_id` | `str` | Identifier for the seed task. |
-| `original_code` | `str` | Clean reference code. |
-| `current_code` | `str` | Latest proposer or solver code. |
-| `role_turn` | `str` | Internal turn marker: `proposer`, `solver`, or `end`. |
-
-## Reward and Grading Logic
-
-DebugZero separates **verification** from **reward shaping**.
-
-- The OpenEnv server is the verifier. It runs submitted code and returns observations.
-- The training layer is the grader. It reads `tests_passed`, `syntax_error`, plausibility, and solve history, then computes scalar rewards.
-
-This is intentional: the same environment can support different reward rubrics without changing the OpenEnv API.
+| Proposer | syntax error or unsafe code | `-0.5` |
+| Proposer | unchanged or effectively no-op code | `0.0` |
+| Proposer | changed code that still passes tests | `0.0` |
+| Proposer | valid failing bug | `1.0 + plausibility_bonus + learnability_bonus` |
+| Solver | syntax error or unsafe code | `-0.5` |
+| Solver | tests pass | `1.0` |
+| Solver | tests fail | `0.0` |
 
-### Server Verifier
+The proposer gets an AST-based plausibility bonus when the edit is small and realistic. The learnability bonus is driven by a rolling solve-rate history with a 20-episode window per seed; the bonus is only active when the current solve rate is in the middle band, roughly `0.2` to `0.8`.
 
-[`DebugzeroEnvironment.step`](server/debugZero_environment.py) always executes code and reports the result, but currently returns `reward=0.0` in the observation. The meaningful reward is computed by the training code from the observation fields.
+That reward shape is why the environment is useful for GRPO: it is not just pass/fail, but it still keeps the signal clean enough to train on.
 
-For proposer actions:
+## Training
 
-- Syntax error: bad mutation.
-- Tests still pass: mutation did not create a useful bug.
-- Tests fail without syntax error: likely useful bug.
+The main training path is the notebook-first workflow in [notebooks/train_colab.ipynb](notebooks/train_colab.ipynb). It:
 
-For solver actions:
+1. installs dependencies
+2. builds the seed bank and verified bug bank
+3. runs the deterministic API controls
+4. runs the live API promise-check probe
+5. runs a pre-training fixed evaluation
+6. trains with TRL GRPO
+7. runs the same fixed evaluation again
+8. saves a before/after plot to `debugzero_model/debugzero_results.png`
 
-- Tests pass without syntax error: solved.
-- Tests fail or syntax error: not solved.
+For a quick local smoke test, use:
 
-### Proposer Reward
-
-Implemented in [`training/rewards.py`](training/rewards.py):
-
-```python
-reward = validity + plausibility + learnability
-```
-
-Components:
-
-| Component | Logic | Reason |
-| --- | --- | --- |
-| `validity` | `-1.0` if syntax error, `+1.0` if tests fail, `0.0` if tests still pass | Rewards executable bugs, rejects broken syntax. |
-| `plausibility` | AST similarity score from `compute_ast_distance` | Rewards small realistic edits over random corruption. |
-| `learnability` | `+1.0` when recent solver success rate is between `0.1` and `0.9` | Rewards bugs that are neither trivial nor impossible. |
-
-The proposer is therefore rewarded for bugs that are:
-
-- valid Python,
-- test-breaking,
-- close to the original AST,
-- useful training examples for the solver.
-
-### Solver Reward
-
-Implemented in [`training/rewards.py`](training/rewards.py):
-
-```python
-solved = tests_passed and not syntax_error
-reward = 1.0 if solved else 0.0
+```bash
+python -X utf8 training/grpo_train.py --dry_run
 ```
 
-Every solver result is recorded in a per-seed rolling deque of length `20`. The proposer uses this history through `get_solve_rate(seed_id)` to estimate whether a bug is learnable.
-
-### Plausibility Grader
-
-Implemented in [`server/plausibility.py`](server/plausibility.py).
+For a real training run, drop `--dry_run` and use the notebook or the same script on a GPU machine.
 
-The plausibility score compares AST dumps of the clean and mutated code using a Levenshtein-style fuzz ratio:
+Model guidance:
 
-| AST similarity ratio | Score | Interpretation |
-| --- | --- | --- |
-| `100` | `0.0` | No edit, not a useful bug. |
-| `85` to `99` | `1.0` | Small realistic mutation. |
-| `50` to `84` | Linear decay down to `0.1` | Medium-sized change. |
-| `< 50` | `0.0` | Too different, likely unrealistic. |
-
-This discourages the proposer from replacing the whole function with nonsense.
-
-### Notebook Reward
-
-The Colab notebook at [`notebooks/train_colab.ipynb`](notebooks/train_colab.ipynb) uses the live OpenEnv server inside the reward function.
-
-For each model completion, it:
-
-1. Extracts Python code from the model output.
-2. Calls `env.reset()`.
-3. Calls `env.step(DebugzeroAction(...))`.
-4. Computes reward from the returned observation.
-
-That means training is connected to the real environment, not a static dataset. The notebook also evaluates baseline and trained policies and saves:
+- Best default for this repo: `unsloth/Qwen2.5-Coder-3B-Instruct`
+- Fast smaller-model experiments: a 1B to 3B coder model
+- If you have more time and memory: a 7B to 8B coder model
 
-- `results/reward_curve.png`
-- `results/loss_curve.png`
-- `results/baseline_vs_trained_reward.png`
-- `results/training_log.csv`
+The solver prompt has two modes:
 
-## Bug Injection Logic
+- `concise` mode is the default for smaller models
+- `full` mode remains available for larger models later
 
-The AST mutation engine lives in [`server/bug_injector.py`](server/bug_injector.py).
+If `bitsandbytes` is available, training uses `adamw_8bit`; otherwise it falls back to `adamw_torch`. The actual GRPO path calls `trainer.train()`, so this is a real training loop rather than a placeholder.
 
-`inject_bug(original_code, proposed_operator)` parses the clean code, applies one AST mutation, unparses the result, and accepts it only if all safety checks pass.
-
-Supported mutation operators:
-
-| Operator | Example behavior |
-| --- | --- |
-| `off_by_one` | Integer constants are shifted by `+1` or `-1`. |
-| `wrong_operator` | Comparisons and arithmetic operators are swapped, such as `<` to `>=` or `+` to `-`. |
-| `wrong_builtin` | Built-ins are swapped, such as `min`/`max`, `any`/`all`, or `sum`/`len`. |
-| `loop_boundary_shift` | `range(n)` becomes `range(n + 1)`, or a two-argument range shifts the start. |
-| `condition_negation` | `if condition` becomes `if not condition`. |
-| `missing_base_case` | A return inside an `if` body is replaced with `pass`. |
-| `slice_boundary_corruption` | Slice lower or upper bounds are shifted. |
-| `variable_swap` | Tuple assignment targets are swapped. |
-
-Accepted mutations must satisfy four checks:
-
-1. Original code parses.
-2. Mutated code is actually different.
-3. Mutated code does not include blocked imports.
-4. Mutated code parses after mutation.
+## Evaluation
 
-## Sandbox and Safety
+The live API smoke test in [eval/api_baseline.py](eval/api_baseline.py) has two layers:
 
-Execution is handled by [`server/executor.py`](server/executor.py).
+1. deterministic controls
+2. live API probing across all 6 seeds
 
-The executor builds:
+The deterministic controls verify that:
 
-```python
-full_code = submitted_code + "\n\n" + tests
-```
+- canonical seed code passes
+- verified bugs fail
+- syntax errors are detected
 
-Then it validates and executes the code in a temporary file with a timeout.
+The live API probe then reports:
 
-Safety checks include:
+- proposer success rate
+- solver success rate
+- proposer syntax-error rate
+- solver syntax-error rate
+- average proposer reward
+- average solver reward
+- one representative success
+- one representative failure
 
-- blocked imports: `os`, `sys`, `subprocess`, `shutil`, `pathlib`
-- blocked built-ins: `__import__`, `eval`, `exec`, `open`
-- AST parsing before execution
-- AST walk to catch direct `Import`, `ImportFrom`, and blocked function calls
-- subprocess timeout, currently `5` seconds
-- temporary directory isolation for each execution
+It also prints which step succeeded for proposer and solver attempts, so you can tell whether the model solved an episode on the first attempt or needed multiple turns.
 
-If code is unsafe but parses, the executor returns:
+To run the probe, set the environment variables and launch the server first:
 
-```text
-Unsafe import detected.
+```powershell
+$env:OPENAI_API_KEY="..."
+$env:OPENAI_MODEL="meta-llama/llama-3.1-8b-instruct"
+$env:DEBUGZERO_ENV_URL="http://localhost:8000"
+python -X utf8 eval/api_baseline.py
 ```
 
-If code does not parse, the executor returns a syntax-error observation.
-
-## Training Pipeline
-
-There are two training paths.
-
-### Recommended: Colab Notebook
-
-Use [`notebooks/train_colab.ipynb`](notebooks/train_colab.ipynb) for the hackathon submission.
-
-It:
-
-1. Installs DebugZero from GitHub.
-2. Starts the packaged OpenEnv FastAPI server, or connects to a remote HF Space URL.
-3. Runs an OpenEnv smoke test through `DebugzeroEnv`.
-4. Builds prompts from live environment resets.
-5. Uses TRL `GRPOTrainer`.
-6. Uses Unsloth when available, with native TRL fallback.
-7. Computes rewards through live `reset` and `step` calls.
-8. Saves plots for the README and final presentation.
-
-### Experimental Script
-
-[`training/grpo_train.py`](training/grpo_train.py) contains an experimental GRPO trainer configuration and the richer reward functions from [`training/rewards.py`](training/rewards.py). It is useful as implementation reference, but the notebook is the clearer end-to-end artifact for judges because it connects directly to the environment and saves visible training evidence.
-
-## Prompt Templates
-
-[`training/dual_role_sampler.py`](training/dual_role_sampler.py) defines two role prompts.
-
-The proposer prompt asks the model to:
-
-- inject an adversarial but plausible bug,
-- keep code syntax-valid,
-- make the function fail tests,
-- return only modified code.
-
-The solver prompt asks the model to:
-
-- inspect buggy code,
-- repair it,
-- return only corrected code.
+The `OPENAI_MODEL` value can be any strong coding model. A capable 7B to 8B class model gives a clearer smoke test than a weak model.
 
-## Evaluation
-
-Tests live under [`eval/`](eval/).
-
-Current checks cover:
-
-- AST mutation behavior:
-  - missing base case,
-  - off-by-one mutation,
-  - loop boundary shift,
-  - wrong built-in,
-  - condition negation,
-  - safety checks.
-- Executor behavior:
-  - safe code passes,
-  - blocked imports are rejected,
-  - syntax errors are rejected,
-  - correct code passes tests,
-  - buggy code fails tests.
-
-There is also a plausibility evaluation scaffold in [`eval/plausibility_eval.py`](eval/plausibility_eval.py) for comparing generated bugs with human-like bugs from the navidadkhah dataset.
-
-Run local checks with:
-
-```bash
-pytest eval
-```
-
-## Running Locally
+## Setup And Run
 
 Install dependencies:
 
@@ -395,134 +191,42 @@ Install dependencies:
 uv sync
 ```
 
-Start the OpenEnv server:
+Start the OpenEnv server from the repo root:
 
 ```bash
-python -m uvicorn server.app:app --host 0.0.0.0 --port 8000
-```
-
-Smoke-test with the client:
-
-```python
-from debugZero.client import DebugzeroEnv
-from debugZero.models import DebugzeroAction
-
-with DebugzeroEnv(base_url="http://localhost:8000") as env:
-    obs = env.reset().observation
-    print(obs.role_next)
-    print(obs.current_code)
-
-    buggy = obs.current_code.replace("distance < threshold", "distance <= threshold")
-    result = env.step(DebugzeroAction(role="proposer", code=buggy))
-    print(result.observation.tests_passed)
+uv run --project . server
 ```
 
-## Inference Checker
-
-[`inference.py`](inference.py) is a standalone environment checker for the submitted Space. It runs full DebugZero episodes through the packaged OpenEnv client and logs every step in a compact format.
-
-Run against the Hugging Face Space:
+You can also run the FastAPI app directly:
 
 ```bash
-set DEBUGZERO_API_URL=https://YOUR-USERNAME-debugzero.hf.space
-set NUM_EPISODES=3
-python inference.py
+uvicorn server.app:app --reload --host 0.0.0.0 --port 8000
 ```
 
-By default, it uses a deterministic sanity policy that:
-
-1. resets the environment,
-2. submits a known failing proposer mutation,
-3. submits the original clean solution as the solver repair,
-4. verifies that proposer failure and solver success are both detected.
-
-To use an LLM through the Hugging Face router or another OpenAI-compatible endpoint:
+Run the API baseline:
 
 ```bash
-set DEBUGZERO_API_URL=https://YOUR-USERNAME-debugzero.hf.space
-set API_BASE_URL=https://router.huggingface.co/v1
-set HF_TOKEN=your_token_here
-set MODEL_NAME=Qwen/Qwen2.5-72B-Instruct
-python inference.py
-```
-
-Useful environment variables:
-
-| Variable | Default | Meaning |
-| --- | --- | --- |
-| `DEBUGZERO_API_URL` | `https://YOUR-USERNAME-debugzero.hf.space` | Remote OpenEnv Space URL. |
-| `LOCAL_IMAGE_NAME` | unset | Docker image name for local OpenEnv image testing. |
-| `NUM_EPISODES` | `3` | Number of episodes to run. |
-| `MAX_STEPS` | `2` | Max steps per episode. DebugZero is normally proposer then solver. |
-| `API_BASE_URL` | `https://router.huggingface.co/v1` | OpenAI-compatible model endpoint. |
-| `API_KEY` / `HF_TOKEN` | unset | Enables LLM mode when present. |
-| `MODEL_NAME` | `Qwen/Qwen2.5-72B-Instruct` | Chat model used for action generation. |
-
-## Repository Structure
-
-```text
-debugZero/
-|-- openenv.yaml                  # OpenEnv manifest
-|-- README.md                     # Project and submission documentation
-|-- models.py                     # Action, observation, and state schemas
-|-- client.py                     # OpenEnv client
-|-- server/
-|   |-- app.py                    # FastAPI OpenEnv app
-|   |-- debugZero_environment.py  # Environment state machine
-|   |-- executor.py               # Code execution and safety checks
-|   |-- bug_injector.py           # AST mutation engine
-|   |-- plausibility.py           # AST similarity grader
-|   `-- requirements.txt          # HF Space server dependencies
-|-- training/
-|   |-- rewards.py                # Proposer and solver reward functions
-|   |-- dual_role_sampler.py      # Prompt templates
-|   `-- grpo_train.py             # Experimental GRPO trainer script
-|-- notebooks/
-|   `-- train_colab.ipynb         # Recommended rerunnable training notebook
-`-- eval/
-    |-- test_bug_injector.py      # Mutation tests
-    |-- test_executor.py          # Executor tests
-    `-- plausibility_eval.py      # Plausibility evaluation scaffold
+python -X utf8 eval/api_baseline.py
 ```
 
-## Deployment Notes
-
-The HF Space runs `server.app:app`, so imports are written to support both:
-
-- top-level Space import mode: `server.app`
-- installed package mode: `debugZero.server.app`
+Run the GRPO smoke test:
 
-Server dependencies for the Space are in [`server/requirements.txt`](server/requirements.txt). The server requires `thefuzz` because `server/plausibility.py` imports it during app startup.
-
-Because the Docker Space serves Uvicorn on port `8000`, the Hugging Face README metadata must include:
-
-```yaml
-sdk: docker
-app_port: 8000
+```bash
+python -X utf8 training/grpo_train.py --dry_run
 ```
 
-After pushing to Hugging Face, confirm:
+The notebook path is the recommended place to do the full training run, especially if you are moving between Colab and a local validation pass.
 
-- the Space builds successfully,
-- `/schema` returns a valid OpenEnv schema,
-- `reset` returns the HumanEval seed code,
-- `step` returns `tests_passed` and `syntax_error`,
-- the README links to the final Space URL and training evidence.
+## Results And Evidence
 
-## Current Limitations and Next Steps
+The training workflow writes a summary plot to `debugzero_model/debugzero_results.png` and prints before/after fixed-eval metrics in the terminal. That gives you a quick way to show whether the solver pass rate and reward moved after training.
 
-Current implementation details to be aware of:
+## Safety
 
-- The server seed is currently a single HumanEval-style function, `HumanEval/0`.
-- The server verifies behavior but does not emit shaped scalar rewards yet. Training computes those externally from observations.
-- Tests are currently bundled in the environment seed. For a stronger benchmark, split public and hidden tests.
-- The AST bug injector exists as a utility, while proposer actions currently submit full mutated code.
-- The training notebook is the preferred proof artifact because it uses the live OpenEnv path and produces plots.
+DebugZero does not execute model-generated code directly in the host process. The executor writes code and tests to a temporary file, runs them in a subprocess, blocks unsafe imports and builtins, and returns a structured result. The OpenEnv server then wraps that environment behind the normal client/server interface.
 
-High-impact next steps:
+## Notes
 
-- Add more HumanEval or curated seed tasks.
-- Move shaped reward metadata into observations for easier external analysis.
-- Add hidden tests and baseline-vs-trained examples to the README.
-- Use the AST injector to generate proposer warm-start examples.
-- Record qualitative before/after solver repairs for the final presentation.
+- The current task bank is deliberately compact so you can see signal quickly.
+- If you want broader training later, the easiest upgrade is to add more `SeedSpec` entries to [seed_bank.py](seed_bank.py).
+- Docker and deployment assets are present, but the current workflow is centered on local validation, API probing, and notebook training.

__init__.py

@@ -1,16 +1,16 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the BSD-style license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""Debugzero Environment."""
-
-from .client import DebugzeroEnv
-from .models import DebugzeroAction, DebugzeroObservation
-
-__all__ = [
-    "DebugzeroAction",
-    "DebugzeroObservation",
-    "DebugzeroEnv",
-]
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""Debugzero Environment."""
+
+from .client import DebugzeroEnv
+from .models import DebugzeroAction, DebugzeroObservation
+
+__all__ = [
+    "DebugzeroAction",
+    "DebugzeroObservation",
+    "DebugzeroEnv",
+]

bug_bank.py

@@ -0,0 +1,140 @@
+from __future__ import annotations
+
+import random
+from dataclasses import dataclass
+
+from seed_bank import SEED_BANK, SeedSpec
+
+try:
+    from server.bug_injector import inject_bug
+    from server.executor import execute_code
+    from server.graders import compute_ast_distance
+except ImportError:
+    from .server.bug_injector import inject_bug
+    from .server.executor import execute_code
+    from .server.graders import compute_ast_distance
+
+
+V1_BUG_OPERATORS = (
+    "wrong_operator",
+    "wrong_builtin",
+    "condition_negation",
+    "off_by_one",
+    "loop_boundary_shift",
+    "slice_boundary_corruption",
+)
+
+MAX_VERIFIED_BUGS_PER_SEED = 4
+HOLDOUT_BUGS_PER_SEED = 1
+MAX_MUTATION_ATTEMPTS = 4
+
+BUG_OPERATOR_PRIORITY = {
+    "loop_boundary_shift": 6,
+    "slice_boundary_corruption": 5,
+    "condition_negation": 4,
+    "wrong_operator": 3,
+    "off_by_one": 2,
+    "wrong_builtin": 1,
+}
+
+
+@dataclass(frozen=True)
+class BugSample:
+    seed_id: str
+    original_code: str
+    buggy_code: str
+    bug_operator: str
+    execution_result: str
+
+
+@dataclass(frozen=True)
+class BugBank:
+    train_samples: tuple[BugSample, ...]
+    eval_samples: tuple[BugSample, ...]
+
+
+def validate_seed(seed: SeedSpec) -> None:
+    result = execute_code(seed.original_code, seed.test)
+    if result.syntax_error or not result.passed:
+        raise ValueError(f"Seed {seed.seed_id} does not pass its canonical tests.")
+
+
+def build_bug_bank(
+    seeds: tuple[SeedSpec, ...] = SEED_BANK,
+    max_verified_bugs_per_seed: int = MAX_VERIFIED_BUGS_PER_SEED,
+    holdout_bugs_per_seed: int = HOLDOUT_BUGS_PER_SEED,
+) -> BugBank:
+    train_samples: list[BugSample] = []
+    eval_samples: list[BugSample] = []
+
+    for seed in seeds:
+        validate_seed(seed)
+        verified_samples = _collect_verified_bugs(seed)
+        verified_samples = sorted(
+            verified_samples,
+            key=lambda sample: _bug_difficulty_score(seed, sample),
+            reverse=True,
+        )
+
+        if len(verified_samples) <= holdout_bugs_per_seed:
+            raise ValueError(
+                f"Seed {seed.seed_id} only produced {len(verified_samples)} verified bugs."
+            )
+
+        eval_samples.extend(verified_samples[:holdout_bugs_per_seed])
+        train_samples.extend(
+            verified_samples[
+                holdout_bugs_per_seed : holdout_bugs_per_seed + max_verified_bugs_per_seed
+            ]
+        )
+
+    return BugBank(
+        train_samples=tuple(train_samples),
+        eval_samples=tuple(eval_samples),
+    )
+
+
+def _collect_verified_bugs(seed: SeedSpec) -> list[BugSample]:
+    verified_samples: list[BugSample] = []
+    seen_codes: set[str] = set()
+
+    for bug_operator in V1_BUG_OPERATORS:
+        for attempt in range(MAX_MUTATION_ATTEMPTS):
+            random.seed(f"{seed.seed_id}:{bug_operator}:{attempt}")
+            buggy_code, changed = inject_bug(seed.original_code, bug_operator)
+            if not changed:
+                continue
+            if buggy_code in seen_codes:
+                continue
+
+            result = execute_code(buggy_code, seed.test)
+            if result.syntax_error or result.passed:
+                continue
+
+            seen_codes.add(buggy_code)
+            verified_samples.append(
+                BugSample(
+                    seed_id=seed.seed_id,
+                    original_code=seed.original_code,
+                    buggy_code=buggy_code,
+                    bug_operator=bug_operator,
+                    execution_result=result.output[:500] if result.output else "",
+                )
+            )
+
+    return verified_samples
+
+
+def _bug_difficulty_score(seed: SeedSpec, sample: BugSample) -> float:
+    operator_score = BUG_OPERATOR_PRIORITY.get(sample.bug_operator, 0)
+    ast_similarity = compute_ast_distance(seed.original_code, sample.buggy_code)
+    execution_lines = _count_nonempty_lines(sample.execution_result)
+
+    # Bias toward bugs that preserve the function shape but still require a real local repair.
+    local_repair_score = ast_similarity
+    execution_signal = min(execution_lines / 4.0, 1.0)
+    return float(operator_score) + local_repair_score + execution_signal
+
+
+def _count_nonempty_lines(text: str) -> int:
+    return sum(1 for line in text.splitlines() if line.strip())

client.py

@@ -1,110 +1,110 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the BSD-style license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""Debugzero Environment Client."""
-
-from typing import Dict
-
-from openenv.core import EnvClient
-from openenv.core.client_types import StepResult
-from openenv.core.env_server.types import State
-
-try:
-    from .models import DebugzeroAction, DebugzeroObservation, DebugzeroState
-except ImportError:
-    from models import DebugzeroAction, DebugzeroObservation, DebugzeroState
-
-
-class DebugzeroEnv(
-    EnvClient[DebugzeroAction, DebugzeroObservation, DebugzeroState]
-):
-    """
-    Client for the DebugZero Environment.
-
-    This client maintains a persistent WebSocket connection to the environment server,
-    enabling efficient multi-step interactions for Proposer/Solver roles.
-    Each client instance has its own dedicated environment session on the server.
-
-    Example:
-        >>> # Connect to a running server
-        >>> with DebugzeroEnv(base_url="http://localhost:8000") as client:
-        ...     result = client.reset()
-        ...     print(result.observation.current_code)
-        ...
-        ...     result = client.step(DebugzeroAction(role="proposer", code="buggy code"))
-        ...     print(result.observation.tests_passed)
-
-    Example with Docker:
-        >>> # Automatically start container and connect
-        >>> client = DebugzeroEnv.from_docker_image("debugZero-env:latest")
-        >>> try:
-        ...     result = client.reset()
-        ...     result = client.step(DebugzeroAction(role="proposer", code="import os"))
-        ... finally:
-        ...     client.close()
-    """
-
-    def _step_payload(self, action: DebugzeroAction) -> Dict:
-        """
-        Convert DebugzeroAction to JSON payload for step message.
-
-        Args:
-            action: DebugzeroAction instance
-
-        Returns:
-            Dictionary representation suitable for JSON encoding
-        """
-        return {
-            "role": action.role,
-            "code": action.code,
-        }
-
-    def _parse_result(self, payload: Dict) -> StepResult[DebugzeroObservation]:
-        """
-        Parse server response into StepResult[DebugzeroObservation].
-
-        Args:
-            payload: JSON response data from server
-
-        Returns:
-            StepResult with DebugzeroObservation
-        """
-        obs_data = payload.get("observation", {})
-        observation = DebugzeroObservation(
-            role_next=obs_data.get("role_next", "proposer"),
-            current_code=obs_data.get("current_code", ""),
-            execution_result=obs_data.get("execution_result", ""),
-            tests_passed=obs_data.get("tests_passed", False),
-            syntax_error=obs_data.get("syntax_error", False),
-            done=payload.get("done", False),
-            reward=payload.get("reward"),
-            metadata=obs_data.get("metadata", {}),
-        )
-
-        return StepResult(
-            observation=observation,
-            reward=payload.get("reward"),
-            done=payload.get("done", False),
-        )
-
-    def _parse_state(self, payload: Dict) -> DebugzeroState:
-        """
-        Parse server response into State object.
-
-        Args:
-            payload: JSON response from state request
-
-        Returns:
-            State object
-        """
-        return DebugzeroState(
-            episode_id=payload.get("episode_id"),
-            step_count=payload.get("step_count", 0),
-            seed_id=payload.get("seed_id", ""),
-            original_code=payload.get("original_code", ""),
-            current_code=payload.get("current_code", ""),
-            role_turn=payload.get("role_turn", "proposer"),
-        )
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""Debugzero Environment Client."""
+
+from typing import Dict
+
+from openenv.core import EnvClient
+from openenv.core.client_types import StepResult
+from openenv.core.env_server.types import State
+
+try:
+    from .models import DebugzeroAction, DebugzeroObservation, DebugzeroState
+except ImportError:
+    from models import DebugzeroAction, DebugzeroObservation, DebugzeroState
+
+
+class DebugzeroEnv(
+    EnvClient[DebugzeroAction, DebugzeroObservation, DebugzeroState]
+):
+    """
+    Client for the DebugZero Environment.
+
+    This client maintains a persistent WebSocket connection to the environment server,
+    enabling efficient multi-step interactions for Proposer/Solver roles.
+    Each client instance has its own dedicated environment session on the server.
+
+    Example:
+        >>> # Connect to a running server
+        >>> with DebugzeroEnv(base_url="http://localhost:8000") as client:
+        ...     result = client.reset()
+        ...     print(result.observation.current_code)
+        ...
+        ...     result = client.step(DebugzeroAction(role="proposer", code="buggy code"))
+        ...     print(result.observation.tests_passed)
+
+    Example with Docker:
+        >>> # Automatically start container and connect
+        >>> client = DebugzeroEnv.from_docker_image("debugZero-env:latest")
+        >>> try:
+        ...     result = client.reset()
+        ...     result = client.step(DebugzeroAction(role="proposer", code="import os"))
+        ... finally:
+        ...     client.close()
+    """
+
+    def _step_payload(self, action: DebugzeroAction) -> Dict:
+        """
+        Convert DebugzeroAction to JSON payload for step message.
+
+        Args:
+            action: DebugzeroAction instance
+
+        Returns:
+            Dictionary representation suitable for JSON encoding
+        """
+        return {
+            "role": action.role,
+            "code": action.code,
+        }
+
+    def _parse_result(self, payload: Dict) -> StepResult[DebugzeroObservation]:
+        """
+        Parse server response into StepResult[DebugzeroObservation].
+
+        Args:
+            payload: JSON response data from server
+
+        Returns:
+            StepResult with DebugzeroObservation
+        """
+        obs_data = payload.get("observation", {})
+        observation = DebugzeroObservation(
+            role_next=obs_data.get("role_next", "proposer"),
+            current_code=obs_data.get("current_code", ""),
+            execution_result=obs_data.get("execution_result", ""),
+            tests_passed=obs_data.get("tests_passed", False),
+            syntax_error=obs_data.get("syntax_error", False),
+            done=payload.get("done", False),
+            reward=payload.get("reward"),
+            metadata=obs_data.get("metadata", {}),
+        )
+
+        return StepResult(
+            observation=observation,
+            reward=payload.get("reward"),
+            done=payload.get("done", False),
+        )
+
+    def _parse_state(self, payload: Dict) -> DebugzeroState:
+        """
+        Parse server response into State object.
+
+        Args:
+            payload: JSON response from state request
+
+        Returns:
+            State object
+        """
+        return DebugzeroState(
+            episode_id=payload.get("episode_id"),
+            step_count=payload.get("step_count", 0),
+            seed_id=payload.get("seed_id", ""),
+            original_code=payload.get("original_code", ""),
+            current_code=payload.get("current_code", ""),
+            role_turn=payload.get("role_turn", "proposer"),
+        )

eval/api_baseline.py

@@ -0,0 +1,367 @@
+import os
+import re
+import sys
+
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+
+from bug_bank import build_bug_bank
+from models import DebugzeroAction
+from seed_bank import SEED_BANK, get_seed_by_id
+from server.bug_injector import infer_bug_operator
+from server.executor import execute_code
+from server.graders import (
+    compute_ast_distance,
+    compute_proposer_reward,
+    compute_solver_reward,
+    is_effectively_unchanged,
+    reset_reward_history,
+)
+from training.dual_role_sampler import sample_proposer_prompt, sample_solver_prompt
+
+
+def extract_python_code(text: str) -> str:
+    match = re.search(r"```(?:python)?\s(.*?)```", text, flags=re.DOTALL)
+    if match:
+        return match.group(1).strip()
+    return text.strip()
+
+
+def execute_candidate(seed_id: str, code: str) -> dict[str, object]:
+    seed = get_seed_by_id(seed_id)
+    result = execute_code(code, seed.test)
+    execution_result = result.output[:500] if result.output else ""
+    return {
+        "tests_passed": result.passed,
+        "syntax_error": result.syntax_error,
+        "unsafe_code": execution_result.startswith("Unsafe import detected."),
+        "execution_result": execution_result,
+    }
+
+
+def classify_proposer_attempt(
+    original_code: str,
+    candidate_code: str,
+    *,
+    tests_passed: bool,
+    syntax_error: bool,
+) -> dict[str, bool]:
+    unchanged_code = is_effectively_unchanged(original_code, candidate_code)
+    valid_bug = (not tests_passed) and (not syntax_error)
+    changed_but_passing = (not unchanged_code) and tests_passed and (not syntax_error)
+    return {
+        "unchanged_code": unchanged_code,
+        "valid_bug": valid_bug,
+        "changed_but_passing": changed_but_passing,
+    }
+
+
+def run_deterministic_controls() -> dict[str, object]:
+    bug_bank = build_bug_bank()
+    controls = []
+
+    print("=" * 80)
+    print("Deterministic controls")
+    print("=" * 80)
+
+    for seed in SEED_BANK:
+        eval_bug = next(sample for sample in bug_bank.eval_samples if sample.seed_id == seed.seed_id)
+        canonical_result = execute_candidate(seed.seed_id, seed.original_code)
+        buggy_result = execute_candidate(seed.seed_id, eval_bug.buggy_code)
+        syntax_result = execute_candidate(seed.seed_id, "def broken(: pass")
+
+        controls.append(
+            {
+                "seed_id": seed.seed_id,
+                "canonical_passes": canonical_result["tests_passed"] and not canonical_result["syntax_error"],
+                "bug_fails": (not buggy_result["tests_passed"]) and not buggy_result["syntax_error"],
+                "syntax_detected": syntax_result["syntax_error"],
+            }
+        )
+
+    canonical_passes = sum(1 for item in controls if item["canonical_passes"])
+    bug_failures = sum(1 for item in controls if item["bug_fails"])
+    syntax_detected = sum(1 for item in controls if item["syntax_detected"])
+
+    summary = {
+        "seed_count": len(SEED_BANK),
+        "canonical_pass_count": canonical_passes,
+        "verified_bug_fail_count": bug_failures,
+        "syntax_detect_count": syntax_detected,
+        "controls": controls,
+        "bug_bank": bug_bank,
+    }
+
+    print(f"Canonical pass count: {canonical_passes}/{len(SEED_BANK)}")
+    print(f"Verified bug fail count: {bug_failures}/{len(SEED_BANK)}")
+    print(f"Syntax detection count: {syntax_detected}/{len(SEED_BANK)}")
+    return summary
+
+
+async def run_live_api_probe(bug_bank) -> dict[str, object] | None:
+    api_key = os.environ.get("OPENAI_API_KEY")
+    base_url = os.environ.get("OPENAI_BASE_URL", "https://openrouter.ai/api/v1")
+    model_name = os.environ.get("OPENAI_MODEL")
+    env_url = os.environ.get("DEBUGZERO_ENV_URL", "http://localhost:8000")
+
+    if not api_key:
+        print("Skipping live API probe: OPENAI_API_KEY is not set.")
+        return None
+    if not model_name:
+        print("Skipping live API probe: OPENAI_MODEL is not set.")
+        return None
+
+    from openai import OpenAI
+    from client import DebugzeroEnv
+
+    client = OpenAI(api_key=api_key, base_url=base_url)
+    env = DebugzeroEnv(base_url=env_url)
+
+    reset_reward_history()
+    proposer_feedback = ""
+    solver_feedback = ""
+
+    metrics = {
+        "episodes": len(SEED_BANK),
+        "proposer_attempts": 0,
+        "solver_attempts": 0,
+        "proposer_successes": 0,
+        "solver_successes": 0,
+        "proposer_step1_successes": 0,
+        "proposer_late_successes": 0,
+        "proposer_valid_bug_attempts": 0,
+        "proposer_unchanged_attempts": 0,
+        "proposer_changed_but_passing_attempts": 0,
+        "proposer_syntax_errors": 0,
+        "solver_syntax_errors": 0,
+        "proposer_rewards": [],
+        "solver_rewards": [],
+        "proposer_bug_family_attempts": {},
+        "episode_details": [],
+        "representative_success": None,
+        "representative_failure": None,
+    }
+
+    print("=" * 80)
+    print("Live API probe")
+    print("=" * 80)
+    print(f"API base URL: {base_url}")
+    print(f"Model: {model_name}")
+
+    try:
+        for episode in range(len(SEED_BANK)):
+            result = await env.reset()
+            obs = result.observation
+            seed_id = obs.metadata.get("seed_id", SEED_BANK[episode].seed_id)
+            original_code = obs.metadata.get("original_code", get_seed_by_id(seed_id).original_code)
+
+            print(f"\nEpisode {episode + 1}/{len(SEED_BANK)} | seed={seed_id}")
+
+            proposer_succeeded = False
+            for proposer_step in range(1, 5):
+                metrics["proposer_attempts"] += 1
+                proposer_prompt = sample_proposer_prompt(obs.current_code)
+                if proposer_feedback:
+                    proposer_prompt = f"{proposer_feedback}\n\n{proposer_prompt}"
+
+                response = client.chat.completions.create(
+                    model=model_name,
+                    max_tokens=1024,
+                    temperature=0.7,
+                    messages=[
+                        {"role": "system", "content": "You are an expert Python coder."},
+                        {"role": "user", "content": proposer_prompt},
+                    ],
+                )
+                proposer_code = extract_python_code(response.choices[0].message.content or "")
+                result = await env.step(DebugzeroAction(role="proposer", code=proposer_code))
+                obs = result.observation
+                proposer_attempt = classify_proposer_attempt(
+                    original_code,
+                    proposer_code,
+                    tests_passed=obs.tests_passed,
+                    syntax_error=obs.syntax_error,
+                )
+
+                proposer_meta = {
+                    "seed_id": seed_id,
+                    "tests_passed": obs.tests_passed,
+                    "syntax_error": obs.syntax_error,
+                    "unsafe_code": obs.execution_result.startswith("Unsafe import detected."),
+                    "unchanged_code": proposer_attempt["unchanged_code"],
+                    "changed_but_passing": proposer_attempt["changed_but_passing"],
+                    "plausibility_score": 0.0
+                    if obs.syntax_error
+                    else compute_ast_distance(original_code, proposer_code),
+                }
+                proposer_reward = compute_proposer_reward(proposer_meta)
+                metrics["proposer_rewards"].append(proposer_reward)
+                likely_bug_family = infer_bug_operator(original_code, proposer_code) or "unknown"
+                if proposer_attempt["valid_bug"]:
+                    metrics["proposer_valid_bug_attempts"] += 1
+                    metrics["proposer_bug_family_attempts"][likely_bug_family] = (
+                        metrics["proposer_bug_family_attempts"].get(likely_bug_family, 0) + 1
+                    )
+                if proposer_attempt["unchanged_code"]:
+                    metrics["proposer_unchanged_attempts"] += 1
+                if proposer_attempt["changed_but_passing"]:
+                    metrics["proposer_changed_but_passing_attempts"] += 1
+
+                if obs.syntax_error:
+                    metrics["proposer_syntax_errors"] += 1
+                    proposer_feedback = "Your last attempt caused a syntax error. Keep the code valid and preserve the signature."
+                elif proposer_attempt["valid_bug"]:
+                    proposer_feedback = "You created a valid failing bug. Keep the change small and realistic."
+                    proposer_succeeded = True
+                    metrics["proposer_successes"] += 1
+                    if proposer_step == 1:
+                        metrics["proposer_step1_successes"] += 1
+                    else:
+                        metrics["proposer_late_successes"] += 1
+                    metrics["episode_details"].append(
+                        {
+                            "seed_id": seed_id,
+                            "role": "proposer",
+                            "step": proposer_step,
+                            "likely_bug_family": likely_bug_family,
+                            "reward": proposer_reward,
+                        }
+                    )
+                    if metrics["representative_success"] is None:
+                        metrics["representative_success"] = {
+                            "role": "proposer",
+                            "seed_id": seed_id,
+                            "reward": proposer_reward,
+                            "code": proposer_code,
+                        }
+                    print(f"  proposer succeeded on step {proposer_step} with reward {proposer_reward:.2f}")
+                    break
+                elif proposer_attempt["unchanged_code"]:
+                    proposer_feedback = (
+                        "Your last attempt did not change behavior. Make exactly one small boundary, "
+                        "comparison, condition, or slice bug."
+                    )
+                else:
+                    proposer_feedback = (
+                        "The tests still passed. Keep exactly one small local edit, but make it "
+                        "behavior-changing."
+                    )
+
+            if not proposer_succeeded:
+                metrics["solver_rewards"].append(0.0)
+                if metrics["representative_failure"] is None:
+                    metrics["representative_failure"] = {
+                        "role": "proposer",
+                        "seed_id": seed_id,
+                        "reason": "failed_to_break_tests",
+                    }
+                print("  proposer did not create a failing bug; solver skipped.")
+                continue
+
+            for solver_step in range(1, 5):
+                metrics["solver_attempts"] += 1
+                solver_prompt = sample_solver_prompt(
+                    obs.current_code,
+                    obs.execution_result,
+                    mode="concise",
+                )
+                if solver_feedback:
+                    solver_prompt = f"{solver_feedback}\n\n{solver_prompt}"
+
+                response = client.chat.completions.create(
+                    model=model_name,
+                    max_tokens=1024,
+                    temperature=0.2,
+                    messages=[
+                        {"role": "system", "content": "You are an expert Python coder."},
+                        {"role": "user", "content": solver_prompt},
+                    ],
+                )
+                solver_code = extract_python_code(response.choices[0].message.content or "")
+                result = await env.step(DebugzeroAction(role="solver", code=solver_code))
+                obs = result.observation
+
+                solver_meta = {
+                    "seed_id": seed_id,
+                    "tests_passed": obs.tests_passed,
+                    "syntax_error": obs.syntax_error,
+                    "unsafe_code": obs.execution_result.startswith("Unsafe import detected."),
+                }
+                solver_reward = compute_solver_reward(solver_meta)
+                metrics["solver_rewards"].append(solver_reward)
+
+                if obs.syntax_error:
+                    metrics["solver_syntax_errors"] += 1
+                    solver_feedback = "The fix caused a syntax error. Return a valid full function."
+                elif obs.tests_passed:
+                    solver_feedback = "The fix passed the tests."
+                    metrics["solver_successes"] += 1
+                    if metrics["representative_success"] is None:
+                        metrics["representative_success"] = {
+                            "role": "solver",
+                            "seed_id": seed_id,
+                            "reward": solver_reward,
+                            "code": solver_code,
+                        }
+                    print(f"  solver succeeded on step {solver_step} with reward {solver_reward:.2f}")
+                    break
+                else:
+                    solver_feedback = "The bug is still present. Focus on the failing behavior in the traceback."
+                    if metrics["representative_failure"] is None:
+                        metrics["representative_failure"] = {
+                            "role": "solver",
+                            "seed_id": seed_id,
+                            "reason": "tests_still_failing",
+                            "execution_result": obs.execution_result,
+                        }
+
+        return metrics
+    finally:
+        await env.close()
+
+
+def print_live_summary(metrics: dict[str, object]) -> None:
+    episodes = int(metrics["episodes"]) or 1
+    proposer_attempts = int(metrics["proposer_attempts"]) or 1
+    solver_attempts = int(metrics["solver_attempts"]) or 1
+    proposer_rewards = metrics["proposer_rewards"]
+    solver_rewards = metrics["solver_rewards"]
+
+    print("\n" + "=" * 80)
+    print("Live API summary")
+    print("=" * 80)
+    print(f"Proposer success rate: {metrics['proposer_successes'] / episodes:.2%}")
+    print(f"Solver success rate:   {metrics['solver_successes'] / episodes:.2%}")
+    print(f"Proposer step-1 success rate: {metrics['proposer_step1_successes'] / episodes:.2%}")
+    print(f"Proposer late success rate:   {metrics['proposer_late_successes'] / episodes:.2%}")
+    print(f"Proposer valid bug rate: {metrics['proposer_valid_bug_attempts'] / proposer_attempts:.2%}")
+    print(f"Proposer unchanged rate: {metrics['proposer_unchanged_attempts'] / proposer_attempts:.2%}")
+    print(
+        f"Proposer changed-pass rate: "
+        f"{metrics['proposer_changed_but_passing_attempts'] / proposer_attempts:.2%}"
+    )
+    print(f"Proposer syntax rate:  {metrics['proposer_syntax_errors'] / proposer_attempts:.2%}")
+    print(f"Solver syntax rate:    {metrics['solver_syntax_errors'] / solver_attempts:.2%}")
+    print(
+        f"Average proposer reward: "
+        f"{(sum(proposer_rewards) / len(proposer_rewards)) if proposer_rewards else 0.0:.2f}"
+    )
+    print(
+        f"Average solver reward:   "
+        f"{(sum(solver_rewards) / len(solver_rewards)) if solver_rewards else 0.0:.2f}"
+    )
+    print(f"Proposer bug families:  {metrics['proposer_bug_family_attempts']}")
+    print(f"Representative success: {metrics['representative_success']}")
+    print(f"Representative failure: {metrics['representative_failure']}")
+
+
+async def main() -> None:
+    control_summary = run_deterministic_controls()
+    metrics = await run_live_api_probe(control_summary["bug_bank"])
+    if metrics is not None:
+        print_live_summary(metrics)
+
+
+if __name__ == "__main__":
+    import asyncio
+
+    asyncio.run(main())

graphify-out/GRAPH_REPORT.md

@@ -1,12 +1,12 @@
-# Graph Report - C:\Users\astra\Desktop\hackon\debugZero  (2026-04-25)
+# Graph Report - C:\Users\astra\Desktop\hackon\ani\DebugZero  (2026-04-26)
 
 ## Corpus Check
-- 17 files · ~18,229 words
+- 16 files · ~23,796 words
 - Verdict: corpus is large enough that graph structure adds value.
 
 ## Summary
-- 123 nodes · 223 edges · 8 communities detected
-- Extraction: 66% EXTRACTED · 34% INFERRED · 0% AMBIGUOUS · INFERRED: 75 edges (avg confidence: 0.61)
+- 145 nodes · 303 edges · 16 communities detected
+- Extraction: 74% EXTRACTED · 26% INFERRED · 0% AMBIGUOUS · INFERRED: 80 edges (avg confidence: 0.7)
 - Token cost: 0 input · 0 output
 
 ## Community Hubs (Navigation)
@@ -18,85 +18,127 @@
 - [[_COMMUNITY_Community 5|Community 5]]
 - [[_COMMUNITY_Community 6|Community 6]]
 - [[_COMMUNITY_Community 7|Community 7]]
+- [[_COMMUNITY_Community 8|Community 8]]
+- [[_COMMUNITY_Community 9|Community 9]]
+- [[_COMMUNITY_Community 10|Community 10]]
+- [[_COMMUNITY_Community 11|Community 11]]
+- [[_COMMUNITY_Community 12|Community 12]]
+- [[_COMMUNITY_Community 13|Community 13]]
+- [[_COMMUNITY_Community 14|Community 14]]
+- [[_COMMUNITY_Community 15|Community 15]]
 
 ## God Nodes (most connected - your core abstractions)
-1. `DebugzeroAction` - 21 edges
-2. `DebugzeroObservation` - 20 edges
-3. `DebugzeroState` - 18 edges
-4. `main()` - 16 edges
+1. `run_live_api_probe()` - 16 edges
+2. `main()` - 15 edges
+3. `DebugzeroObservation` - 14 edges
+4. `DebugzeroAction` - 13 edges
 5. `DebugzeroEnvironment` - 13 edges
-6. `BugInjectorVisitor` - 11 edges
-7. `DebugzeroEnv` - 10 edges
-8. `inject_bug()` - 9 edges
-9. `execute_code()` - 8 edges
-10. `get_model_action()` - 6 edges
+6. `DebugzeroState` - 12 edges
+7. `evaluate_proposer_fixed_set()` - 12 edges
+8. `BugInjectorVisitor` - 11 edges
+9. `DebugzeroEnv` - 10 edges
+10. `run_workflow()` - 10 edges
 
 ## Surprising Connections (you probably didn't know these)
-- `DebugzeroEnv` --uses--> `LLMAction`  [INFERRED]
-  C:\Users\astra\Desktop\hackon\debugZero\client.py → C:\Users\astra\Desktop\hackon\debugZero\inference.py
-- `DebugzeroEnv` --calls--> `make_env()`  [INFERRED]
-  C:\Users\astra\Desktop\hackon\debugZero\client.py → C:\Users\astra\Desktop\hackon\debugZero\inference.py
-- `LLMAction` --uses--> `DebugzeroAction`  [INFERRED]
-  C:\Users\astra\Desktop\hackon\debugZero\inference.py → C:\Users\astra\Desktop\hackon\debugZero\models.py
-- `deterministic_action()` --calls--> `DebugzeroAction`  [INFERRED]
-  C:\Users\astra\Desktop\hackon\debugZero\inference.py → C:\Users\astra\Desktop\hackon\debugZero\models.py
-- `get_model_action()` --calls--> `DebugzeroAction`  [INFERRED]
-  C:\Users\astra\Desktop\hackon\debugZero\inference.py → C:\Users\astra\Desktop\hackon\debugZero\models.py
+- `build_bug_bank()` --calls--> `run_deterministic_controls()`  [INFERRED]
+  C:\Users\astra\Desktop\hackon\ani\DebugZero\bug_bank.py → C:\Users\astra\Desktop\hackon\ani\DebugZero\eval\api_baseline.py
+- `build_bug_bank()` --calls--> `create_dataset()`  [INFERRED]
+  C:\Users\astra\Desktop\hackon\ani\DebugZero\bug_bank.py → C:\Users\astra\Desktop\hackon\ani\DebugZero\training\grpo_train.py
+- `_collect_verified_bugs()` --calls--> `inject_bug()`  [INFERRED]
+  C:\Users\astra\Desktop\hackon\ani\DebugZero\bug_bank.py → C:\Users\astra\Desktop\hackon\ani\DebugZero\server\bug_injector.py
+- `_bug_difficulty_score()` --calls--> `compute_ast_distance()`  [INFERRED]
+  C:\Users\astra\Desktop\hackon\ani\DebugZero\bug_bank.py → C:\Users\astra\Desktop\hackon\ani\DebugZero\server\graders.py
+- `DebugzeroEnv` --uses--> `DebugzeroObservation`  [INFERRED]
+  C:\Users\astra\Desktop\hackon\ani\DebugZero\client.py → C:\Users\astra\Desktop\hackon\ani\DebugZero\models.py
 
 ## Communities
 
 ### Community 0 - "Community 0"
-Cohesion: 0.15
-Nodes (23): Action, Entry point for direct execution via uv run or python -m.      This function ena, DebugzeroEnv, Client for the DebugZero Environment.      This client maintains a persistent We, Client for the DebugZero Environment.      This client maintains a persistent We, Convert DebugzeroAction to JSON payload for step message.          Args:, Convert DebugzeroAction to JSON payload for step message.          Args:, Parse server response into StepResult[DebugzeroObservation].          Args: (+15 more)
+Cohesion: 0.13
+Nodes (12): BugInjectorVisitor, infer_bug_operator(), _infer_condition_negation(), _infer_loop_boundary_shift(), _infer_off_by_one(), _infer_slice_boundary_corruption(), _infer_wrong_builtin(), _infer_wrong_operator() (+4 more)
 
 ### Community 1 - "Community 1"
-Cohesion: 0.14
-Nodes (27): BaseModel, build_openai_client(), build_prompt(), _code_preview(), compact_action_string(), deterministic_action(), deterministic_bug(), _execution_preview() (+19 more)
+Cohesion: 0.22
+Nodes (15): build_mixed_role_dataset(), build_tiny_local_model_and_tokenizer(), build_weighted_proposer_rows(), choose_proposer_bug_focus(), create_dataset(), create_trainer(), get_training_profile(), get_trl_classes() (+7 more)
 
 ### Community 2 - "Community 2"
-Cohesion: 0.13
-Nodes (10): BugInjectorVisitor, inject_bug(), is_safe_injection(), 4 critical checks:     - parse succeeds      - mutation actually changed code, test_condition_negation(), test_loop_boundary_shift(), test_missing_base_case(), test_off_by_one() (+2 more)
+Cohesion: 0.23
+Nodes (12): _bug_difficulty_score(), BugBank, BugSample, build_bug_bank(), _collect_verified_bugs(), _count_nonempty_lines(), validate_seed(), execute_code() (+4 more)
 
 ### Community 3 - "Community 3"
-Cohesion: 0.15
-Nodes (6): main(), Entry point for direct execution via uv run or python -m.      This function ena, compute_ast_distance(), evaluate_navidadkhah_plausibility(), Offline evaluation of generated bugs against the navidadkhah 25k bug dataset., Computes the string similarity distance between the AST dumps of the original
+Cohesion: 0.23
+Nodes (13): classify_proposer_attempt(), execute_candidate(), extract_python_code(), main(), print_live_summary(), run_deterministic_controls(), run_live_api_probe(), sample_proposer_prompt() (+5 more)
 
 ### Community 4 - "Community 4"
-Cohesion: 0.2
-Nodes (12): Dual-role DebugZero Environment wrapping a Python sandbox execution     for Prop, _build_full_code(), execute_code(), ExecutionResult, _first_function_name(), is_safe(), Check if the code contains any blocked imports strings.      Also performs a qu, Executes the provided python code alongside its tests in an isolated subprocess. (+4 more)
+Cohesion: 0.31
+Nodes (12): call_env_method(), clamp_score(), classify_proposer_attempt(), compact_action_string(), extract_env_error(), log_end(), log_start(), log_step() (+4 more)
 
 ### Community 5 - "Community 5"
-Cohesion: 0.33
-Nodes (7): create_dataset(), main(), reward_fn(), compute_proposer_reward(), compute_solver_reward(), get_solve_rate(), record_solve_result()
+Cohesion: 0.27
+Nodes (10): Action, DebugzeroEnv, Client for the DebugZero Environment.      This client maintains a persistent, Convert DebugzeroAction to JSON payload for step message.          Args:, Parse server response into State object.          Args:             payload:, DebugzeroAction, DebugzeroState, Action for the DebugZero environment representing the Proposer or Solver inputs. (+2 more)
 
 ### Community 6 - "Community 6"
-Cohesion: 0.67
-Nodes (0): 
+Cohesion: 0.29
+Nodes (2): Dual-role DebugZero Environment wrapping a Python sandbox execution     for Pro, SeedSpec
 
 ### Community 7 - "Community 7"
+Cohesion: 0.48
+Nodes (2): DebugzeroEnvironment, Environment
+
+### Community 8 - "Community 8"
+Cohesion: 0.29
+Nodes (7): compute_ast_distance(), is_effectively_unchanged(), Computes the string similarity distance between the AST dumps of the original, evaluate_proposer_fixed_set(), summarize_proposer_by_bug_family(), summarize_proposer_by_seed(), summarize_proposer_results()
+
+### Community 9 - "Community 9"
+Cohesion: 0.48
+Nodes (7): completion_to_text(), evaluate_bug_sample(), execute_candidate(), extract_python_code(), prop_rew(), solv_rew(), get_seed_by_id()
+
+### Community 10 - "Community 10"
+Cohesion: 0.4
+Nodes (2): main(), Entry point for direct execution via uv run or python -m.      This function e
+
+### Community 11 - "Community 11"
+Cohesion: 0.47
+Nodes (5): compute_proposer_reward(), compute_solver_reward(), get_solve_rate(), record_solve_result(), reset_reward_history()
+
+### Community 12 - "Community 12"
+Cohesion: 0.5
+Nodes (4): Parse server response into StepResult[DebugzeroObservation].          Args:, DebugzeroObservation, Observation from the DebugZero environment following sandbox execution., Observation
+
+### Community 13 - "Community 13"
+Cohesion: 0.67
+Nodes (3): evaluate_solver_fixed_set(), generate_code(), summarize_solver_results()
+
+### Community 14 - "Community 14"
 Cohesion: 1.0
-Nodes (1): Executes the provided python code alongside its tests in an isolated subprocess.
+Nodes (1): Computes the string similarity distance between the AST dumps of the original
+
+### Community 15 - "Community 15"
+Cohesion: 1.0
+Nodes (1): Computes the string similarity distance between the AST dumps of the original
 
 ## Knowledge Gaps
-- **9 isolated node(s):** `Action for the DebugZero environment representing the Proposer or Solver inputs.`, `Observation from the DebugZero environment following sandbox execution.`, `State for the DebugZero environment, extending default state with seed context.`, `Offline evaluation of generated bugs against the navidadkhah 25k bug dataset.`, `4 critical checks:     - parse succeeds      - mutation actually changed code` (+4 more)
+- **9 isolated node(s):** `Action for the DebugZero environment representing the Proposer or Solver inputs.`, `Observation from the DebugZero environment following sandbox execution.`, `State for the DebugZero environment, extending default state with seed context.`, `4 critical checks:     - parse succeeds      - mutation actually changed code`, `Check if the code contains any blocked imports strings.      Also performs a qu` (+4 more)
   These have ≤1 connection - possible missing edges or undocumented components.
-- **Thin community `Community 7`** (1 nodes): `Executes the provided python code alongside its tests in an isolated subprocess.`
+- **Thin community `Community 14`** (1 nodes): `Computes the string similarity distance between the AST dumps of the original`
+  Too small to be a meaningful cluster - may be noise or needs more connections extracted.
+- **Thin community `Community 15`** (1 nodes): `Computes the string similarity distance between the AST dumps of the original`
   Too small to be a meaningful cluster - may be noise or needs more connections extracted.
 
 ## Suggested Questions
 _Questions this graph is uniquely positioned to answer:_
 
-- **Why does `DebugzeroAction` connect `Community 0` to `Community 1`, `Community 3`, `Community 4`?**
-  _High betweenness centrality (0.216) - this node is a cross-community bridge._
-- **Why does `DebugzeroEnvironment` connect `Community 0` to `Community 3`, `Community 4`?**
-  _High betweenness centrality (0.209) - this node is a cross-community bridge._
-- **Are the 18 inferred relationships involving `DebugzeroAction` (e.g. with `DebugzeroEnv` and `Client for the DebugZero Environment.      This client maintains a persistent We`) actually correct?**
-  _`DebugzeroAction` has 18 INFERRED edges - model-reasoned connections that need verification._
-- **Are the 17 inferred relationships involving `DebugzeroObservation` (e.g. with `DebugzeroEnv` and `Client for the DebugZero Environment.      This client maintains a persistent We`) actually correct?**
-  _`DebugzeroObservation` has 17 INFERRED edges - model-reasoned connections that need verification._
-- **Are the 15 inferred relationships involving `DebugzeroState` (e.g. with `DebugzeroEnv` and `Client for the DebugZero Environment.      This client maintains a persistent We`) actually correct?**
-  _`DebugzeroState` has 15 INFERRED edges - model-reasoned connections that need verification._
-- **Are the 7 inferred relationships involving `DebugzeroEnvironment` (e.g. with `Entry point for direct execution via uv run or python -m.      This function ena` and `DebugzeroAction`) actually correct?**
-  _`DebugzeroEnvironment` has 7 INFERRED edges - model-reasoned connections that need verification._
-- **What connects `Action for the DebugZero environment representing the Proposer or Solver inputs.`, `Observation from the DebugZero environment following sandbox execution.`, `State for the DebugZero environment, extending default state with seed context.` to the rest of the system?**
-  _9 weakly-connected nodes found - possible documentation gaps or missing edges._
+- **Why does `run_live_api_probe()` connect `Community 3` to `Community 0`, `Community 5`, `Community 7`, `Community 8`, `Community 9`, `Community 11`?**
+  _High betweenness centrality (0.267) - this node is a cross-community bridge._
+- **Why does `main()` connect `Community 4` to `Community 8`, `Community 11`, `Community 5`?**
+  _High betweenness centrality (0.131) - this node is a cross-community bridge._
+- **Why does `DebugzeroAction` connect `Community 5` to `Community 3`, `Community 4`, `Community 6`, `Community 7`, `Community 10`, `Community 12`?**
+  _High betweenness centrality (0.125) - this node is a cross-community bridge._
+- **Are the 12 inferred relationships involving `run_live_api_probe()` (e.g. with `DebugzeroEnv` and `reset_reward_history()`) actually correct?**
+  _`run_live_api_probe()` has 12 INFERRED edges - model-reasoned connections that need verification._
+- **Are the 5 inferred relationships involving `main()` (e.g. with `reset_reward_history()` and `DebugzeroAction`) actually correct?**
+  _`main()` has 5 INFERRED edges - model-reasoned connections that need verification._
+- **Are the 11 inferred relationships involving `DebugzeroObservation` (e.g. with `DebugzeroEnv` and `Client for the DebugZero Environment.      This client maintains a persistent`) actually correct?**
+  _`DebugzeroObservation` has 11 INFERRED edges - model-reasoned connections that need verification._
+- **Are the 10 inferred relationships involving `DebugzeroAction` (e.g. with `DebugzeroEnv` and `Client for the DebugZero Environment.      This client maintains a persistent`) actually correct?**
+  _`DebugzeroAction` has 10 INFERRED edges - model-reasoned connections that need verification._

graphify-out/cache/055047d6cfcb60857a41382dcbaa312e3a3c8f9b2d73eef2080ebc404ce9e853.json

@@ -0,0 +1 @@
+{"nodes": [{"id": "c_users_astra_desktop_hackon_ani_debugzero_server_tasks_py", "label": "tasks.py", "file_type": "code", "source_file": "C:\\Users\\astra\\Desktop\\hackon\\ani\\DebugZero\\server\\tasks.py", "source_location": "L1"}, {"id": "tasks_seedspec", "label": "SeedSpec", "file_type": "code", "source_file": "C:\\Users\\astra\\Desktop\\hackon\\ani\\DebugZero\\server\\tasks.py", "source_location": "L7"}, {"id": "tasks_original_code", "label": "original_code()", "file_type": "code", "source_file": "C:\\Users\\astra\\Desktop\\hackon\\ani\\DebugZero\\server\\tasks.py", "source_location": "L15"}, {"id": "tasks_get_seed_by_id", "label": "get_seed_by_id()", "file_type": "code", "source_file": "C:\\Users\\astra\\Desktop\\hackon\\ani\\DebugZero\\server\\tasks.py", "source_location": "L139"}, {"id": "tasks_legacy_seed_dict", "label": "legacy_seed_dict()", "file_type": "code", "source_file": "C:\\Users\\astra\\Desktop\\hackon\\ani\\DebugZero\\server\\tasks.py", "source_location": "L143"}], "edges": [{"source": "c_users_astra_desktop_hackon_ani_debugzero_server_tasks_py", "target": "dataclasses", "relation": "imports_from", "confidence": "EXTRACTED", "source_file": "C:\\Users\\astra\\Desktop\\hackon\\ani\\DebugZero\\server\\tasks.py", "source_location": "L3", "weight": 1.0}, {"source": "c_users_astra_desktop_hackon_ani_debugzero_server_tasks_py", "target": "tasks_seedspec", "relation": "contains", "confidence": "EXTRACTED", "source_file": "C:\\Users\\astra\\Desktop\\hackon\\ani\\DebugZero\\server\\tasks.py", "source_location": "L7", "weight": 1.0}, {"source": "c_users_astra_desktop_hackon_ani_debugzero_server_tasks_py", "target": "tasks_original_code", "relation": "contains", "confidence": "EXTRACTED", "source_file": "C:\\Users\\astra\\Desktop\\hackon\\ani\\DebugZero\\server\\tasks.py", "source_location": "L15", "weight": 1.0}, {"source": "c_users_astra_desktop_hackon_ani_debugzero_server_tasks_py", "target": "tasks_get_seed_by_id", "relation": "contains", "confidence": "EXTRACTED", "source_file": "C:\\Users\\astra\\Desktop\\hackon\\ani\\DebugZero\\server\\tasks.py", "source_location": "L139", "weight": 1.0}, {"source": "c_users_astra_desktop_hackon_ani_debugzero_server_tasks_py", "target": "tasks_legacy_seed_dict", "relation": "contains", "confidence": "EXTRACTED", "source_file": "C:\\Users\\astra\\Desktop\\hackon\\ani\\DebugZero\\server\\tasks.py", "source_location": "L143", "weight": 1.0}], "raw_calls": []}

graphify-out/cache/109212a64b8d08b5cd75f1b351b47be4581db6846b8a3c8cfbf80902849c53af.json

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"C:\\Users\\astra\\Desktop\\hackon\\ani\\DebugZero\\eval\\api_baseline.py", "source_location": "L197"}, {"caller_nid": "api_baseline_run_live_api_probe", "callee": "infer_bug_operator", "source_file": "C:\\Users\\astra\\Desktop\\hackon\\ani\\DebugZero\\eval\\api_baseline.py", "source_location": "L198"}, {"caller_nid": "api_baseline_run_live_api_probe", "callee": "get", "source_file": "C:\\Users\\astra\\Desktop\\hackon\\ani\\DebugZero\\eval\\api_baseline.py", "source_location": "L202"}, {"caller_nid": "api_baseline_run_live_api_probe", "callee": "append", "source_file": "C:\\Users\\astra\\Desktop\\hackon\\ani\\DebugZero\\eval\\api_baseline.py", "source_location": "L220"}, {"caller_nid": "api_baseline_run_live_api_probe", "callee": "print", "source_file": "C:\\Users\\astra\\Desktop\\hackon\\ani\\DebugZero\\eval\\api_baseline.py", "source_location": "L236"}, {"caller_nid": "api_baseline_run_live_api_probe", "callee": "append", "source_file": "C:\\Users\\astra\\Desktop\\hackon\\ani\\DebugZero\\eval\\api_baseline.py", "source_location": "L250"}, {"caller_nid": "api_baseline_run_live_api_probe", "callee": "print", "source_file": "C:\\Users\\astra\\Desktop\\hackon\\ani\\DebugZero\\eval\\api_baseline.py", "source_location": "L257"}, {"caller_nid": "api_baseline_run_live_api_probe", "callee": "range", "source_file": "C:\\Users\\astra\\Desktop\\hackon\\ani\\DebugZero\\eval\\api_baseline.py", "source_location": "L260"}, {"caller_nid": "api_baseline_run_live_api_probe", "callee": "sample_solver_prompt", "source_file": "C:\\Users\\astra\\Desktop\\hackon\\ani\\DebugZero\\eval\\api_baseline.py", "source_location": "L262"}, {"caller_nid": "api_baseline_run_live_api_probe", "callee": "create", "source_file": "C:\\Users\\astra\\Desktop\\hackon\\ani\\DebugZero\\eval\\api_baseline.py", "source_location": "L270"}, {"caller_nid": "api_baseline_run_live_api_probe", "callee": "step", "source_file": "C:\\Users\\astra\\Desktop\\hackon\\ani\\DebugZero\\eval\\api_baseline.py", "source_location": "L280"}, {"caller_nid": "api_baseline_run_live_api_probe", "callee": "DebugzeroAction", "source_file": "C:\\Users\\astra\\Desktop\\hackon\\ani\\DebugZero\\eval\\api_baseline.py", "source_location": "L280"}, {"caller_nid": "api_baseline_run_live_api_probe", "callee": "startswith", "source_file": "C:\\Users\\astra\\Desktop\\hackon\\ani\\DebugZero\\eval\\api_baseline.py", "source_location": "L287"}, {"caller_nid": "api_baseline_run_live_api_probe", "callee": "compute_solver_reward", "source_file": "C:\\Users\\astra\\Desktop\\hackon\\ani\\DebugZero\\eval\\api_baseline.py", "source_location": "L289"}, {"caller_nid": "api_baseline_run_live_api_probe", "callee": "append", "source_file": "C:\\Users\\astra\\Desktop\\hackon\\ani\\DebugZero\\eval\\api_baseline.py", "source_location": "L290"}, {"caller_nid": "api_baseline_run_live_api_probe", "callee": "print", "source_file": "C:\\Users\\astra\\Desktop\\hackon\\ani\\DebugZero\\eval\\api_baseline.py", "source_location": "L305"}, {"caller_nid": "api_baseline_run_live_api_probe", "callee": "close", "source_file": "C:\\Users\\astra\\Desktop\\hackon\\ani\\DebugZero\\eval\\api_baseline.py", "source_location": "L319"}, {"caller_nid": "api_baseline_print_live_summary", "callee": "int", "source_file": "C:\\Users\\astra\\Desktop\\hackon\\ani\\DebugZero\\eval\\api_baseline.py", "source_location": "L323"}, {"caller_nid": "api_baseline_print_live_summary", "callee": "int", "source_file": "C:\\Users\\astra\\Desktop\\hackon\\ani\\DebugZero\\eval\\api_baseline.py", "source_location": "L324"}, {"caller_nid": "api_baseline_print_live_summary", "callee": "int", "source_file": "C:\\Users\\astra\\Desktop\\hackon\\ani\\DebugZero\\eval\\api_baseline.py", "source_location": "L325"}, {"caller_nid": "api_baseline_print_live_summary", "callee": "print", "source_file": 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implementation.md

@@ -0,0 +1,352 @@
+# DebugZero Implementation Guide
+
+## What This Repository Is
+
+`DebugZero` is an `OpenEnv`-style debugging environment built around a two-role loop:
+
+- a `proposer` takes correct Python code and tries to inject a realistic logical bug
+- a `solver` receives buggy code and tries to repair it
+
+The repository is designed to support three things at once:
+
+1. a runnable environment server
+2. a GRPO-style training pipeline
+3. an API-based evaluation harness for smoke-testing the environment before training
+
+The current setup is intentionally small and deterministic so it is easy to verify, train on, and explain in a hackathon setting.
+
+## High-Level Environment Workflow
+
+At a high level, one episode works like this:
+
+1. The environment resets onto one curated seed task from the seed bank.
+2. The `proposer` sees the clean reference implementation and tries to make one small logical mistake.
+3. The environment executes the proposed code against the task tests.
+4. If the code now fails tests in a valid way, the proposer gets rewarded and the environment hands the buggy code to the `solver`.
+5. The `solver` tries to repair the code.
+6. The environment executes the solver output against the same tests.
+7. If the fix passes, the solver gets rewarded and the episode ends.
+
+This gives a clean adversarial/self-improvement loop:
+
+- the proposer learns to create realistic, test-breaking bugs
+- the solver learns to reverse those bugs
+- the reward functions are role-specific
+
+## Project Architecture
+
+The project is split into a few clear layers.
+
+### 1. Task and Bug Data
+
+- `seed_bank.py` defines the curated clean tasks
+- `bug_bank.py` builds verified buggy variants from those tasks
+
+### 2. Environment Runtime
+
+- `server/debugZero_environment.py` is the main environment state machine
+- `server/executor.py` runs generated code safely against tests
+- `server/bug_injector.py` provides AST-based bug mutations
+- `server/plausibility.py` scores whether a proposer bug looks realistic
+- `server/app.py` exposes the environment through a FastAPI/OpenEnv server
+
+### 3. Shared Interface
+
+- `models.py` defines the request/response data models shared across client and server
+- `client.py` provides a small client wrapper for interacting with the environment
+
+### 4. Training
+
+- `training/dual_role_sampler.py` builds role-specific prompts
+- `training/rewards.py` computes proposer and solver rewards
+- `training/grpo_train.py` builds the dataset, runs evaluation, and launches GRPO training
+
+### 5. Evaluation
+
+- `eval/api_baseline.py` is the main smoke-test harness
+
+It first runs deterministic controls, then optionally runs a live API probe using a model served through an OpenAI-compatible endpoint.
+
+## Data Design
+
+The current data setup is not Faker-style random generation. It is a curated-and-verified pipeline.
+
+### Seed Bank
+
+The environment uses a fixed curated set of six Python tasks:
+
+- `HumanEval/0` -> `has_close_elements`
+- `DebugZero/1` -> `sum_to_n`
+- `DebugZero/2` -> `middle_slice`
+- `DebugZero/3` -> `is_non_decreasing`
+- `DebugZero/4` -> `count_nonempty`
+- `DebugZero/5` -> `running_max`
+
+Each seed contains:
+
+- `seed_id`
+- `entrypoint`
+- `prompt`
+- `canonical_solution`
+- `test`
+
+### Bug Bank
+
+The bug bank is generated programmatically from the seed bank using AST mutation operators. Only bugs that meet all of the following conditions are kept:
+
+- the code actually changed
+- the mutated code still parses
+- the code is considered safe enough to execute
+- the mutated code fails the seed's tests
+
+The current bug bank split is:
+
+- `18` training bug samples
+- `6` evaluation holdout bug samples
+
+This gives the training loop a deterministic but nontrivial set of bug/fix tasks.
+
+## Reward Design
+
+The reward functions are intentionally role-specific.
+
+### Proposer Reward
+
+The proposer is rewarded for creating realistic failing bugs, not just for changing code.
+
+Current behavior:
+
+- syntax error or unsafe output -> `-0.5`
+- unchanged code -> `0.0`
+- changed code that still passes tests -> `0.0`
+- valid failing bug -> `1.0 + plausibility_bonus + learnability_bonus`
+
+Where:
+
+- `plausibility_bonus` rewards bugs that look more like realistic programmer mistakes
+- `learnability_bonus` favors bugs that are neither trivial nor impossible for the solver
+
+### Solver Reward
+
+The solver reward is intentionally simpler:
+
+- syntax error -> `-0.5`
+- failed fix -> `0.0`
+- passing fix -> `1.0`
+
+This makes the solver side easier to optimize and easier to explain.
+
+## Training Workflow
+
+The training path is centered on GRPO-style optimization.
+
+### What Gets Trained
+
+The training dataset is mixed-role:
+
+- solver-heavy by design
+- still includes proposer rows so both roles are represented
+
+The current mixed-role build uses:
+
+- `18` solver rows
+- `9` proposer rows
+- `27` rows total
+
+### Training Loop Shape
+
+`training/grpo_train.py` does the following:
+
+1. builds the verified bug bank
+2. constructs the mixed-role dataset
+3. formats prompts through the dual-role sampler
+4. evaluates pre-training behavior on a fixed holdout set
+5. runs GRPO training
+6. evaluates post-training behavior
+7. saves a small results plot
+
+There is also a `--dry_run` path for quick local smoke testing.
+
+### Recommended Models
+
+Best default for this environment:
+
+- `unsloth/Qwen2.5-Coder-3B-Instruct`
+
+Reasonable alternatives:
+
+- `1B` to `2B` coder models for faster cheap runs
+- `7B` to `8B` coder/instruct models for stronger evaluation if compute allows
+
+In practice:
+
+- `1B` to `3B` is the most sensible training range for this repo's size and task complexity
+- `7B` to `8B` is useful as a stronger API smoke-test model or a higher-end final experiment
+
+## Evaluation Workflow
+
+The main evaluation script is `eval/api_baseline.py`.
+
+It has two phases.
+
+### 1. Deterministic Controls
+
+Before any live model call, it verifies that:
+
+- canonical code passes
+- verified buggy code fails
+- obvious syntax errors are detected
+
+This is the fast check that the environment has real signal.
+
+### 2. Live API Probe
+
+If `OPENAI_API_KEY` and `OPENAI_MODEL` are present, it then runs a multi-episode proposer/solver loop over the seed bank.
+
+It reports:
+
+- proposer success rate
+- solver success rate
+- proposer valid bug rate
+- proposer unchanged rate
+- proposer changed-but-passing rate
+- proposer syntax rate
+- solver syntax rate
+- average proposer reward
+- average solver reward
+- one representative success
+- one representative failure
+
+This is the main pre-training sanity check.
+
+## End-to-End Runtime Flow
+
+If we trace one full path through the system, it looks like this:
+
+1. `seed_bank.py` provides a clean seed task.
+2. `server/debugZero_environment.py` resets onto that seed.
+3. A proposer model generates code from a proposer prompt built by `training/dual_role_sampler.py` or `eval/api_baseline.py`.
+4. `server/executor.py` runs the candidate code against the seed tests.
+5. `training/rewards.py` computes proposer reward.
+6. If the proposer created a valid failing bug, the solver gets a repair prompt.
+7. The solver generates repaired code.
+8. `server/executor.py` runs the repair candidate.
+9. `training/rewards.py` computes solver reward.
+10. `training/grpo_train.py` uses these rewards during GRPO training or fixed evaluation.
+
+## Tracked Python Files
+
+Below is what each currently tracked `.py` file is doing.
+
+### Live Runtime And Training Files
+
+#### [__init__.py](./__init__.py)
+
+Marks the repository root package so imports can work cleanly in package-style execution.
+
+#### [seed_bank.py](./seed_bank.py)
+
+Defines the curated seed task bank. Each seed includes the prompt, canonical solution, test harness, and function entrypoint. This is the base dataset for the whole environment.
+
+#### [bug_bank.py](./bug_bank.py)
+
+Builds and stores verified buggy samples from the seed bank. It filters mutations down to samples that are syntactically valid, meaningfully changed, safe to run, and test-failing. It also splits them into training and evaluation holdouts.
+
+#### [models.py](./models.py)
+
+Defines the shared Pydantic models used across client/server communication. This includes the environment action and observation structures.
+
+#### [client.py](./client.py)
+
+Provides a small client interface for talking to the environment server using the shared models. It is the clean consumer-side entrypoint for external interaction.
+
+#### [eval/api_baseline.py](./eval/api_baseline.py)
+
+Runs the main smoke-test and evaluation workflow. It verifies deterministic controls first, then runs a live proposer/solver API probe across the seed bank when API credentials are available.
+
+#### [server/__init__.py](./server/__init__.py)
+
+Marks the `server` package.
+
+#### [server/app.py](./server/app.py)
+
+Creates the FastAPI/OpenEnv application and wires the environment into the server layer so external tools can call it.
+
+#### [server/bug_injector.py](./server/bug_injector.py)
+
+Contains the AST mutation logic used to generate plausible logical bugs from clean code. This is the mutation engine behind the verified bug bank.
+
+#### [server/debugZero_environment.py](./server/debugZero_environment.py)
+
+Implements the main environment state machine. It handles reset, proposer steps, solver steps, execution feedback, seed progression, and observation construction.
+
+This is the heart of the repo.
+
+#### [server/executor.py](./server/executor.py)
+
+Runs model-generated Python code in a constrained execution path with safety checks and test execution. This is the file that turns raw code into pass/fail execution signals.
+
+#### [server/plausibility.py](./server/plausibility.py)
+
+Scores how realistic a proposer bug looks. That plausibility signal is used as part of proposer reward shaping.
+
+#### [training/dual_role_sampler.py](./training/dual_role_sampler.py)
+
+Builds the role-specific prompts used during training. It formats proposer prompts for bug injection and solver prompts for bug repair, including the concise solver mode used for smaller models.
+
+#### [training/grpo_train.py](./training/grpo_train.py)
+
+The main training entrypoint. It builds datasets, prepares the trainer, evaluates before and after training, and saves training artifacts such as the results plot.
+
+#### [training/rewards.py](./training/rewards.py)
+
+Defines the reward logic for proposer and solver outputs. It is the main source of learning signal for GRPO.
+
+### Tracked Template / Scaffolding Files
+
+These files are tracked in git, but they are not part of the live DebugZero runtime. They are template assets checked into the repo under `.claude/...` and appear to be scaffolding for generating OpenEnv environments.
+
+#### [`.claude/skills/generate-openenv-env/assets/openenv_env_template/__init__.py`](./.claude/skills/generate-openenv-env/assets/openenv_env_template/__init__.py)
+
+Template package marker for generated environments.
+
+#### [`.claude/skills/generate-openenv-env/assets/openenv_env_template/client.py`](./.claude/skills/generate-openenv-env/assets/openenv_env_template/client.py)
+
+Template client file used when scaffolding a new OpenEnv environment.
+
+#### [`.claude/skills/generate-openenv-env/assets/openenv_env_template/models.py`](./.claude/skills/generate-openenv-env/assets/openenv_env_template/models.py)
+
+Template shared models file used by the environment generator.
+
+#### [`.claude/skills/generate-openenv-env/assets/openenv_env_template/server/__ENV_NAME___environment.py`](./.claude/skills/generate-openenv-env/assets/openenv_env_template/server/__ENV_NAME___environment.py)
+
+Template environment implementation stub for newly generated environments.
+
+#### [`.claude/skills/generate-openenv-env/assets/openenv_env_template/server/__init__.py`](./.claude/skills/generate-openenv-env/assets/openenv_env_template/server/__init__.py)
+
+Template server package marker for generated environments.
+
+#### [`.claude/skills/generate-openenv-env/assets/openenv_env_template/server/app.py`](./.claude/skills/generate-openenv-env/assets/openenv_env_template/server/app.py)
+
+Template FastAPI/OpenEnv app file for generated environments.
+
+## What This Repo Is Optimized For
+
+This repository is optimized for:
+
+- explaining the environment clearly
+- proving that the environment has real reward signal
+- running a small but defensible GRPO training loop
+- showing before/after improvement, especially on the solver side
+
+It is not optimized for:
+
+- huge-scale dataset diversity
+- deployment polish
+- extremely large benchmark coverage
+
+That tradeoff is deliberate. The current design favors clarity, determinism, and hackathon-speed iteration.
+
+## In One Sentence
+
+`DebugZero` is a compact self-play debugging environment where one model learns to inject realistic bugs, another learns to fix them, and the repo is structured so that this loop can be tested, trained, and explained cleanly.

inference.py

@@ -1,382 +1,178 @@
 import asyncio
+import inspect
 import json
 import os
 import re
-import textwrap
-from typing import Any, Dict, List, Optional
-
-try:
-    from dotenv import load_dotenv
-except ImportError:
-    def load_dotenv() -> None:
-        return None
-
-try:
-    from openai import OpenAI
-except ImportError:
-    OpenAI = None  # type: ignore[assignment]
-
-from pydantic import BaseModel, Field
-
-try:
-    from debugZero.client import DebugzeroEnv
-    from debugZero.models import DebugzeroAction
-except ImportError:
-    from client import DebugzeroEnv
-    from models import DebugzeroAction
-
+import sys
+from typing import Any, List, Optional
+
+from dotenv import load_dotenv
+from openai import OpenAI
+
+from client import DebugzeroEnv
+from models import DebugzeroAction
+from server.graders import (
+    compute_ast_distance,
+    compute_proposer_reward,
+    compute_solver_reward,
+    is_effectively_unchanged,
+    reset_reward_history,
+)
+from training.dual_role_sampler import sample_proposer_prompt, sample_solver_prompt
 
 load_dotenv()
 
 
-class LLMAction(BaseModel):
-    role: str = Field(..., description="Either proposer or solver")
-    code: str = Field(..., description="Complete Python function code")
-
-
-API_BASE_URL = os.getenv("API_BASE_URL", "https://router.huggingface.co/v1")
-MODEL_NAME = os.getenv("MODEL_NAME", "Qwen/Qwen2.5-72B-Instruct")
-API_KEY = os.getenv("API_KEY") or os.getenv("HF_TOKEN")
+API_BASE_URL = os.getenv("API_BASE_URL") or os.getenv("OPENAI_BASE_URL", "https://openrouter.ai/api/v1")
+MODEL_NAME = os.getenv("MODEL_NAME") or os.getenv("OPENAI_MODEL", "meta-llama/llama-3.1-8b-instruct")
+API_KEY = os.getenv("API_KEY") or os.getenv("OPENAI_API_KEY") or os.getenv("HF_TOKEN")
 
 LOCAL_IMAGE_NAME = os.getenv("LOCAL_IMAGE_NAME")
-DEBUGZERO_API_URL = os.getenv("DEBUGZERO_API_URL", "https://YOUR-USERNAME-debugzero.hf.space")
+ENV_URL = os.getenv("DEBUGZERO_ENV_URL", "http://localhost:8000")
+TASK_NAME = os.getenv("DEBUGZERO_TASK", "debugging-self-play")
+BENCHMARK = os.getenv("DEBUGZERO_BENCHMARK", "debugzero")
+BUG_FOCUS = os.getenv("DEBUGZERO_BUG_FOCUS")
 
 NUM_EPISODES = int(os.getenv("NUM_EPISODES", "3"))
-MAX_STEPS = int(os.getenv("MAX_STEPS", "2"))
-TEMPERATURE = float(os.getenv("TEMPERATURE", "0.0"))
-MAX_TOKENS = int(os.getenv("MAX_TOKENS", "2048"))
-
-
-def build_openai_client() -> Any:
-    if not API_KEY:
-        return None
-    if OpenAI is None:
-        print("[SYSTEM ERROR] API key is set but the openai package is not installed.", flush=True)
-        return None
-    if not API_BASE_URL.startswith(("http://", "https://")):
-        print(
-            "[SYSTEM ERROR] API_BASE_URL must be an HTTP URL. "
-            "It looks like a key/token was placed there, so deterministic mode will run instead.",
-            flush=True,
-        )
-        return None
-    return OpenAI(base_url=API_BASE_URL, api_key=API_KEY)
-
-
-def model_dump(obj: Any) -> Dict[str, Any]:
-    if obj is None:
-        return {}
-    if hasattr(obj, "model_dump"):
-        return obj.model_dump()
-    if hasattr(obj, "dict"):
-        return obj.dict()
-    if isinstance(obj, dict):
-        return obj
-    return {
-        key: getattr(obj, key)
-        for key in dir(obj)
-        if not key.startswith("_") and not callable(getattr(obj, key))
-    }
-
-
-def unwrap_observation(result: Any) -> Any:
-    return getattr(result, "observation", result)
-
+MAX_STEPS = int(os.getenv("MAX_STEPS", "8"))
+MAX_PROPOSER_STEPS = int(os.getenv("MAX_PROPOSER_STEPS", str(max(1, MAX_STEPS // 2))))
+MAX_SOLVER_STEPS = int(os.getenv("MAX_SOLVER_STEPS", str(max(1, MAX_STEPS - MAX_PROPOSER_STEPS))))
+PROPOSER_TEMPERATURE = float(os.getenv("PROPOSER_TEMPERATURE", "0.7"))
+SOLVER_TEMPERATURE = float(os.getenv("SOLVER_TEMPERATURE", "0.2"))
+MAX_TOKENS = int(os.getenv("MAX_TOKENS", "1024"))
 
-def unwrap_done(result: Any, obs: Any) -> bool:
-    return bool(getattr(result, "done", getattr(obs, "done", False)))
 
+def extract_python_code(text: str) -> str:
+    match = re.search(r"```(?:python)?\s*(.*?)```", text, flags=re.DOTALL)
+    if match:
+        return match.group(1).strip()
+    return text.strip()
 
-def unwrap_reward(result: Any, obs: Any) -> float:
-    value = getattr(result, "reward", getattr(obs, "reward", 0.0))
-    return float(value or 0.0)
 
+def compact_action_string(role: str, code: str) -> str:
+    obj = {"role": role, "code": code}
+    return json.dumps(obj, separators=(",", ":"), ensure_ascii=False)
 
-def sanitize_code(text: str) -> str:
-    text = (text or "").strip()
-    fenced = re.search(r"```(?:python)?\s*(.*?)```", text, flags=re.DOTALL | re.IGNORECASE)
-    if fenced:
-        text = fenced.group(1).strip()
-    if text.startswith("```"):
-        text = text.split("\n", 1)[-1].strip()
-    if text.endswith("```"):
-        text = text.rsplit("\n", 1)[0].strip()
-    return text.strip().strip("`")
 
+def log_start(task: str, env: str, model: str) -> None:
+    print(f"[START] task={task} env={env} model={model}", flush=True)
 
-def compact_action_string(action: DebugzeroAction) -> str:
-    return json.dumps(
-        {"role": action.role, "code": action.code},
-        separators=(",", ":"),
-        ensure_ascii=False,
-    )
-
-
-def _indent_block(text: str, spaces: int = 4) -> str:
-    prefix = " " * spaces
-    return "\n".join(f"{prefix}{line}" if line else prefix for line in text.splitlines())
-
-
-def _code_preview(code: str, max_lines: int = 18) -> str:
-    lines = (code or "").strip().splitlines()
-    if len(lines) > max_lines:
-        lines = lines[:max_lines] + [f"... ({len(lines) - max_lines} more lines)"]
-    return _indent_block("\n".join(lines) or "(empty)")
 
-
-def _execution_preview(execution_result: str, max_lines: int = 8) -> str:
-    text = (execution_result or "").strip()
-    if not text:
-        return "    (none)"
-
-    lines = [line.rstrip() for line in text.splitlines() if line.strip()]
-    useful = [
-        line
-        for line in lines
-        if "AssertionError" in line
-        or "SyntaxError" in line
-        or "Unsafe import" in line
-        or "Execution timed out" in line
-        or "assert candidate" in line
-        or "check(" in line
-    ]
-    selected = useful if useful else lines[-max_lines:]
-    if len(selected) > max_lines:
-        selected = selected[-max_lines:]
-    return _indent_block("\n".join(selected))
-
-
-def log_start(episode: int, env_url: str, model: str, mode: str) -> None:
+def log_step(step: int, action: str, reward: float, done: bool, error: Optional[str]) -> None:
+    error_val = error if error is not None else "null"
+    action_str = action.replace("\n", "\\n")
     print(
-        "\n"
-        + "=" * 72
-        + f"\nEpisode {episode} | env=debugZero | mode={mode}\n"
-        + f"URL: {env_url}\n"
-        + f"Model: {model}\n"
-        + "=" * 72,
-        flush=True,
-    )
-
-
-def log_step(
-    step: int,
-    action: DebugzeroAction,
-    reward: float,
-    done: bool,
-    tests_passed: Optional[bool],
-    syntax_error: Optional[bool],
-    execution_result: str,
-) -> None:
-    role = action.role
-    status = "PASS" if tests_passed else "FAIL"
-    if syntax_error:
-        status = "SYNTAX_ERROR"
-    done_text = "true" if done else "false"
-    print(
-        f"\n[Step {step}] role={role} status={status}\n"
-        f"  reward={reward:.2f} | done={done_text} | "
-        f"tests_passed={tests_passed} | syntax_error={syntax_error}\n"
-        "  submitted code:\n"
-        f"{_code_preview(action.code)}\n"
-        "  execution summary:\n"
-        f"{_execution_preview(execution_result)}",
+        f"[STEP] step={step} action={action_str} reward={reward:.2f} done={str(done).lower()} error={error_val}",
         flush=True,
     )
 
 
 def log_end(success: bool, steps: int, score: float, rewards: List[float]) -> None:
-    rewards_str = ",".join(f"{reward:.2f}" for reward in rewards)
+    rewards_str = ",".join(f"{r:.2f}" for r in rewards)
     print(
-        "\n"
-        + "-" * 72
-        + f"\nEpisode result: success={str(success).lower()} | "
-        f"steps={steps} | score={score:.4f} | rewards=[{rewards_str}]\n"
-        + "-" * 72,
+        f"[END] success={str(success).lower()} steps={steps} score={score:.4f} rewards={rewards_str}",
         flush=True,
     )
 
 
-def score_transition(role: str, obs: Any, original_code: str, submitted_code: str) -> float:
-    tests_passed = bool(getattr(obs, "tests_passed", False))
-    syntax_error = bool(getattr(obs, "syntax_error", False))
+def summarize_error(text: str, max_chars: int = 220) -> str:
+    cleaned = " ".join(text.strip().split())
+    if not cleaned:
+        return "null"
+    if len(cleaned) <= max_chars:
+        return cleaned
+    return cleaned[: max_chars - 3].rstrip() + "..."
 
-    if role == "proposer":
-        if syntax_error:
-            return -1.0
-        if submitted_code.strip() == original_code.strip():
-            return -0.5
-        return 1.0 if not tests_passed else 0.0
-
-    if role == "solver":
-        if syntax_error:
-            return -1.0
-        return 1.0 if tests_passed else 0.0
-
-    return 0.0
-
-
-def success_from_episode(last_obs: Any, rewards: List[float], proposer_broke_tests: bool) -> bool:
-    return (
-        proposer_broke_tests
-        and bool(getattr(last_obs, "tests_passed", False))
-        and not bool(getattr(last_obs, "syntax_error", True))
-        and bool(rewards)
-        and rewards[-1] > 0
-    )
 
+def extract_env_error(observation: Any) -> Optional[str]:
+    execution_result = getattr(observation, "execution_result", "") or ""
+    if not execution_result:
+        return None
+    if getattr(observation, "syntax_error", False):
+        return summarize_error(execution_result)
+    if execution_result.startswith("Unsafe import detected."):
+        return execution_result
+    if not getattr(observation, "tests_passed", False):
+        return summarize_error(execution_result)
+    return None
 
-def build_prompt(obs_dict: Dict[str, Any], original_code: str, history: List[str]) -> str:
-    role_next = obs_dict.get("role_next", "proposer")
-    current_code = obs_dict.get("current_code", "")
-    execution_result = obs_dict.get("execution_result", "")
-    history_block = "\n".join(history[-4:]) if history else "None"
-
-    if role_next == "proposer":
-        objective = textwrap.dedent(
-            """
-            You are the Proposer in DebugZero.
-            Create one realistic bug in the clean function.
-            The code must remain syntactically valid Python and should fail tests.
-            Prefer small human-like edits such as boundary, comparison, operator, or return-condition mistakes.
-            Do not use imports, file IO, eval, exec, open, os, sys, subprocess, shutil, or pathlib.
-            """
-        ).strip()
-    else:
-        objective = textwrap.dedent(
-            """
-            You are the Solver in DebugZero.
-            Repair the current buggy function so it passes the hidden environment tests.
-            Return the complete corrected function.
-            Do not use imports, file IO, eval, exec, open, os, sys, subprocess, shutil, or pathlib.
-            """
-        ).strip()
-
-    return textwrap.dedent(
-        f"""
-        You are controlling a live OpenEnv environment named DebugZero.
-
-        Current role: {role_next}
-
-        Objective:
-        {objective}
-
-        Original clean code:
-        ```python
-        {original_code}
-        ```
-
-        Current environment observation:
-        {json.dumps(obs_dict, indent=2, ensure_ascii=False)}
-
-        Last execution result:
-        {execution_result or "None"}
-
-        Previous actions:
-        {history_block}
-
-        Return exactly one valid JSON object with this schema:
-        {{
-          "role": "{role_next}",
-          "code": "<complete Python function, no markdown fences>"
-        }}
-        """
-    ).strip()
-
-
-def parse_model_content(content: str, fallback_role: str, fallback_code: str) -> LLMAction:
-    content = (content or "{}").strip()
-    if content.startswith("```"):
-        content = content.split("\n", 1)[-1]
-    if content.endswith("```"):
-        content = content.rsplit("\n", 1)[0]
-    content = content.strip()
 
-    try:
-        parsed = json.loads(content)
-    except json.JSONDecodeError:
-        parsed = {"role": fallback_role, "code": content}
+def classify_proposer_attempt(
+    original_code: str,
+    candidate_code: str,
+    *,
+    tests_passed: bool,
+    syntax_error: bool,
+) -> dict[str, bool]:
+    unchanged_code = is_effectively_unchanged(original_code, candidate_code)
+    valid_bug = (not tests_passed) and (not syntax_error)
+    changed_but_passing = (not unchanged_code) and tests_passed and (not syntax_error)
+    return {
+        "unchanged_code": unchanged_code,
+        "valid_bug": valid_bug,
+        "changed_but_passing": changed_but_passing,
+    }
 
-    if not isinstance(parsed, dict):
-        parsed = {"role": fallback_role, "code": fallback_code}
 
-    parsed["role"] = str(parsed.get("role") or fallback_role).strip().lower()
-    parsed["code"] = sanitize_code(str(parsed.get("code") or fallback_code))
-    return LLMAction(**parsed)
+def clamp_score(score: float) -> float:
+    return max(0.0001, min(0.9999, float(score)))
 
 
-def deterministic_bug(clean_code: str) -> str:
-    replacements = [
-        ("idx != idx2", "idx == idx2"),
-        ("distance < threshold", "distance <= threshold"),
-        ("return True", "return False"),
-        ("return False", "return True"),
-    ]
-    for old, new in replacements:
-        if old in clean_code:
-            return clean_code.replace(old, new, 1)
-    return clean_code + "\n# no-op fallback mutation\n"
+async def maybe_await(value: Any) -> Any:
+    if inspect.isawaitable(value):
+        return await value
+    return value
 
 
-def deterministic_action(obs_dict: Dict[str, Any], original_code: str) -> DebugzeroAction:
-    role = str(obs_dict.get("role_next") or "proposer").lower()
-    if role == "solver":
-        return DebugzeroAction(role="solver", code=original_code)
-    return DebugzeroAction(role="proposer", code=deterministic_bug(original_code))
+async def call_env_method(obj: Any, method_name: str, *args: Any) -> Any:
+    method = getattr(obj, method_name)
+    result = method(*args)
+    return await maybe_await(result)
 
 
-def get_model_action(
-    client: Any,
-    obs_dict: Dict[str, Any],
-    original_code: str,
-    history: List[str],
-) -> DebugzeroAction:
-    role = str(obs_dict.get("role_next") or "proposer").lower()
-    fallback = deterministic_action(obs_dict, original_code)
+def get_model_code(
+    client: OpenAI,
+    *,
+    role: str,
+    current_code: str,
+    execution_result: str,
+    feedback: str,
+) -> str:
+    if role == "proposer":
+        prompt = sample_proposer_prompt(current_code, bug_focus=BUG_FOCUS)
+        temperature = PROPOSER_TEMPERATURE
+    else:
+        prompt = sample_solver_prompt(current_code, execution_result, mode="concise")
+        temperature = SOLVER_TEMPERATURE
 
-    if client is None:
-        return fallback
+    if feedback:
+        prompt = f"{feedback}\n\n{prompt}"
 
-    prompt = build_prompt(obs_dict, original_code, history)
     response = client.chat.completions.create(
         model=MODEL_NAME,
         messages=[
-            {
-                "role": "system",
-                "content": "You are a careful code-debugging agent. Return only valid JSON.",
-            },
+            {"role": "system", "content": "You are an expert Python coder."},
             {"role": "user", "content": prompt},
         ],
-        temperature=TEMPERATURE,
+        temperature=temperature,
         max_tokens=MAX_TOKENS,
-        response_format={"type": "json_object"},
     )
 
-    content = response.choices[0].message.content or "{}"
-    llm_action = parse_model_content(content, role, fallback.code)
-
-    if llm_action.role not in {"proposer", "solver"}:
-        llm_action.role = role
-    if llm_action.role != role and role in {"proposer", "solver"}:
-        llm_action.role = role
-
-    return DebugzeroAction(role=llm_action.role, code=sanitize_code(llm_action.code))
-
-
-async def maybe_await(value: Any) -> Any:
-    if asyncio.iscoroutine(value):
-        return await value
-    return value
+    return extract_python_code(response.choices[0].message.content or "")
 
 
 async def make_env() -> Any:
+    max_retries = 30
+
     if LOCAL_IMAGE_NAME:
-        max_retries = 30
         for attempt in range(max_retries):
             try:
-                return await maybe_await(DebugzeroEnv.from_docker_image(LOCAL_IMAGE_NAME))
+                env = DebugzeroEnv.from_docker_image(LOCAL_IMAGE_NAME)
+                return await maybe_await(env)
             except Exception as exc:
                 print(
-                    f"[SYSTEM ERROR] Docker env start failed attempt={attempt + 1}/{max_retries}: {exc}",
+                    f"[SYSTEM ERROR] Failed to start Docker environment (attempt {attempt + 1}/{max_retries}): {exc}",
+                    file=sys.stderr,
                     flush=True,
                 )
                 if attempt < max_retries - 1:
@@ -384,17 +180,13 @@ async def make_env() -> Any:
                 else:
                     raise
 
-    max_retries = 30
     for attempt in range(max_retries):
         try:
-            env = DebugzeroEnv(base_url=DEBUGZERO_API_URL)
-            if hasattr(env, "sync"):
-                return env.sync()
-            return env
+            return DebugzeroEnv(base_url=ENV_URL)
         except Exception as exc:
             print(
-                f"[SYSTEM ERROR] Env connection to {DEBUGZERO_API_URL} failed "
-                f"attempt={attempt + 1}/{max_retries}: {exc}",
+                f"[SYSTEM ERROR] Env connection to {ENV_URL} failed (attempt {attempt + 1}/{max_retries}): {exc}",
+                file=sys.stderr,
                 flush=True,
             )
             if attempt < max_retries - 1:
@@ -403,104 +195,169 @@ async def make_env() -> Any:
                 raise
 
 
-async def reset_env(env: Any) -> Any:
-    if hasattr(env, "reset_async"):
-        return await env.reset_async()
-    return await maybe_await(env.reset())
-
-
-async def step_env(env: Any, action: DebugzeroAction) -> Any:
-    if hasattr(env, "step_async"):
-        return await env.step_async(action)
-    return await maybe_await(env.step(action))
-
-
 async def main() -> None:
-    client = build_openai_client()
-    mode = "llm" if client is not None else "deterministic"
+    if not API_KEY:
+        print("[SYSTEM ERROR] Missing API key. Set API_KEY, OPENAI_API_KEY, or HF_TOKEN.", file=sys.stderr, flush=True)
+        return
 
+    client = OpenAI(base_url=API_BASE_URL, api_key=API_KEY)
     env = None
+
     try:
         env = await make_env()
+        reset_reward_history()
 
-        for episode in range(1, NUM_EPISODES + 1):
-            history: List[str] = []
+        for _episode in range(1, NUM_EPISODES + 1):
             rewards: List[float] = []
             steps_taken = 0
-            last_obs = None
-            original_code = ""
-            proposer_broke_tests = False
+            proposer_feedback = ""
+            solver_feedback = ""
+            proposer_succeeded = False
+            success = False
+            score = 0.0001
 
-            log_start(episode=episode, env_url=DEBUGZERO_API_URL, model=MODEL_NAME, mode=mode)
+            log_start(task=TASK_NAME, env=BENCHMARK, model=MODEL_NAME)
 
             try:
-                reset_result = await reset_env(env)
-                obs = unwrap_observation(reset_result)
-                last_obs = obs
-                original_code = str(getattr(obs, "current_code", "") or "")
-                done = unwrap_done(reset_result, obs)
-
-                for step in range(1, MAX_STEPS + 1):
-                    if done:
-                        break
+                result = await call_env_method(env, "reset")
+                obs = result.observation
+                original_code = obs.metadata.get("original_code", obs.current_code)
 
-                    obs_dict = model_dump(obs)
-                    action = await asyncio.to_thread(get_model_action, client, obs_dict, original_code, history)
-                    action_str = compact_action_string(action)
-
-                    result = await step_env(env, action)
-                    obs = unwrap_observation(result)
-                    last_obs = obs
-                    done = unwrap_done(result, obs)
+                for _proposer_step in range(1, MAX_PROPOSER_STEPS + 1):
+                    if steps_taken >= MAX_STEPS:
+                        break
+                    steps_taken += 1
+
+                    try:
+                        proposer_code = await asyncio.to_thread(
+                            get_model_code,
+                            client,
+                            role="proposer",
+                            current_code=obs.current_code,
+                            execution_result=obs.execution_result,
+                            feedback=proposer_feedback,
+                        )
+                    except Exception as exc:
+                        print(f"[SYSTEM ERROR] Proposer generation failed: {exc}", file=sys.stderr, flush=True)
+                        proposer_code = obs.current_code
+
+                    action = DebugzeroAction(role="proposer", code=proposer_code)
+                    result = await call_env_method(env, "step", action)
+                    obs = result.observation
+
+                    proposer_attempt = classify_proposer_attempt(
+                        original_code,
+                        proposer_code,
+                        tests_passed=obs.tests_passed,
+                        syntax_error=obs.syntax_error,
+                    )
 
-                    env_reward = unwrap_reward(result, obs)
-                    shaped_reward = score_transition(action.role, obs, original_code, action.code)
-                    reward = shaped_reward if env_reward == 0.0 else env_reward
+                    reward = compute_proposer_reward(
+                        {
+                            "seed_id": obs.metadata.get("seed_id", ""),
+                            "tests_passed": obs.tests_passed,
+                            "syntax_error": obs.syntax_error,
+                            "unsafe_code": obs.execution_result.startswith("Unsafe import detected."),
+                            "unchanged_code": proposer_attempt["unchanged_code"],
+                            "changed_but_passing": proposer_attempt["changed_but_passing"],
+                            "plausibility_score": 0.0
+                            if obs.syntax_error
+                            else compute_ast_distance(original_code, proposer_code),
+                        }
+                    )
                     rewards.append(reward)
-                    steps_taken = step
-
-                    if action.role == "proposer" and shaped_reward > 0:
-                        proposer_broke_tests = True
-
-                    tests_passed = getattr(obs, "tests_passed", None)
-                    syntax_error = getattr(obs, "syntax_error", None)
-                    execution_result = str(getattr(obs, "execution_result", "") or "")
 
                     log_step(
-                        step=step,
-                        action=action,
+                        step=steps_taken,
+                        action=compact_action_string("proposer", proposer_code),
                         reward=reward,
-                        done=done,
-                        tests_passed=tests_passed,
-                        syntax_error=syntax_error,
-                        execution_result=execution_result,
-                    )
-
-                    history.append(
-                        f"Step {step}: role={action.role} reward={reward:.2f} "
-                        f"tests_passed={tests_passed} syntax_error={syntax_error}"
+                        done=bool(getattr(result, "done", False)),
+                        error=extract_env_error(obs),
                     )
 
-                    if done:
+                    if obs.syntax_error:
+                        proposer_feedback = (
+                            "Your last attempt caused a syntax error. Keep the code valid and preserve the signature."
+                        )
+                    elif proposer_attempt["valid_bug"]:
+                        proposer_succeeded = True
+                        score = 0.5000
                         break
-
-                success = success_from_episode(last_obs, rewards, proposer_broke_tests)
-                score = 1.0 if success else max(0.0, sum(rewards) / max(1, len(rewards)))
-                score = max(0.0001, min(0.9999, float(score)))
+                    elif proposer_attempt["unchanged_code"]:
+                        proposer_feedback = (
+                            "Your last attempt did not change behavior. Make exactly one small boundary, "
+                            "comparison, condition, or slice bug."
+                        )
+                    else:
+                        proposer_feedback = (
+                            "The tests still passed. Keep exactly one small local edit, but make it "
+                            "behavior-changing."
+                        )
+
+                if proposer_succeeded:
+                    for _solver_step in range(1, MAX_SOLVER_STEPS + 1):
+                        if steps_taken >= MAX_STEPS:
+                            break
+                        steps_taken += 1
+
+                        try:
+                            solver_code = await asyncio.to_thread(
+                                get_model_code,
+                                client,
+                                role="solver",
+                                current_code=obs.current_code,
+                                execution_result=obs.execution_result,
+                                feedback=solver_feedback,
+                            )
+                        except Exception as exc:
+                            print(f"[SYSTEM ERROR] Solver generation failed: {exc}", file=sys.stderr, flush=True)
+                            solver_code = obs.current_code
+
+                        action = DebugzeroAction(role="solver", code=solver_code)
+                        result = await call_env_method(env, "step", action)
+                        obs = result.observation
+
+                        reward = compute_solver_reward(
+                            {
+                                "seed_id": obs.metadata.get("seed_id", ""),
+                                "tests_passed": obs.tests_passed,
+                                "syntax_error": obs.syntax_error,
+                                "unsafe_code": obs.execution_result.startswith("Unsafe import detected."),
+                            }
+                        )
+                        rewards.append(reward)
+
+                        log_step(
+                            step=steps_taken,
+                            action=compact_action_string("solver", solver_code),
+                            reward=reward,
+                            done=bool(getattr(result, "done", False)),
+                            error=extract_env_error(obs),
+                        )
+
+                        if obs.syntax_error:
+                            solver_feedback = "The fix caused a syntax error. Return a valid full function."
+                        elif obs.tests_passed:
+                            success = True
+                            score = 0.9999
+                            break
+                        else:
+                            solver_feedback = (
+                                "The bug is still present. Focus on the failing behavior in the traceback."
+                            )
+
+                log_end(success=success, steps=steps_taken, score=clamp_score(score), rewards=rewards)
 
             except Exception as exc:
-                print(f"[SYSTEM ERROR] {exc}", flush=True)
-                success = False
-                score = 0.0001
-            finally:
-                log_end(success=success, steps=steps_taken, score=score, rewards=rewards)
+                print(f"[SYSTEM ERROR] {exc}", file=sys.stderr, flush=True)
+                log_end(success=False, steps=steps_taken, score=clamp_score(score), rewards=rewards)
 
     except Exception as exc:
-        print(f"[SYSTEM ERROR] {exc}", flush=True)
+        print(f"[SYSTEM ERROR] {exc}", file=sys.stderr, flush=True)
     finally:
         try:
             if env is not None and hasattr(env, "close"):
-                await maybe_await(env.close())
+                await call_env_method(env, "close")
         except Exception:
             pass
 
@@ -508,5 +365,10 @@ async def main() -> None:
 if __name__ == "__main__":
     try:
         asyncio.run(main())
+        sys.exit(0)
     except Exception as exc:
-        print(f"[CRITICAL VALIDATION ERROR] {exc}", flush=True)
+        print(f"[CRITICAL VALIDATION ERROR] {exc}", file=sys.stderr, flush=True)
+        sys.exit(0)
+    except BaseException as base_exc:
+        print(f"[BASE EXCEPTION] {base_exc}", file=sys.stderr, flush=True)
+        sys.exit(0)

models.py

@@ -1,39 +1,39 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the BSD-style license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Data models for the DebugZero Environment.
-
-The debugZero environment implements the Absolute Zero paradigm for debugging self-play.
-"""
-
-from openenv.core.env_server.types import Action, Observation, State
-from pydantic import Field
-from typing import Optional
-
-
-class DebugzeroAction(Action):
-    """Action for the DebugZero environment representing the Proposer or Solver inputs."""
-
-    role: str = Field(..., description="Role taking action: 'proposer' or 'solver'")
-    code: str = Field(..., description="Code injected (by proposer) or fixed (by solver)")
-
-
-class DebugzeroObservation(Observation):
-    """Observation from the DebugZero environment following sandbox execution."""
-
-    role_next: str = Field(default="proposer", description="The role supposed to play next")
-    current_code: str = Field(default="", description="The current state of the python code")
-    execution_result: str = Field(default="", description="Result of evaluating tests in the sandbox")
-    tests_passed: bool = Field(default=False, description="Whether the tests passed")
-    syntax_error: bool = Field(default=False, description="Whether the code had a parse/syntax error")
-
-class DebugzeroState(State):
-    """State for the DebugZero environment, extending default state with seed context."""
-    seed_id: str = Field(default="", description="ID of the HumanEval function")
-    original_code: str = Field(default="", description="Original clean seed code")
-    current_code: str = Field(default="", description="Current code after Proposer/Solver turn")
-    role_turn: str = Field(default="proposer", description="Current turn's role")
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Data models for the DebugZero Environment.
+
+The debugZero environment implements the Absolute Zero paradigm for debugging self-play.
+"""
+
+from openenv.core.env_server.types import Action, Observation, State
+from pydantic import Field
+from typing import Optional
+
+
+class DebugzeroAction(Action):
+    """Action for the DebugZero environment representing the Proposer or Solver inputs."""
+
+    role: str = Field(..., description="Role taking action: 'proposer' or 'solver'")
+    code: str = Field(..., description="Code injected (by proposer) or fixed (by solver)")
+
+
+class DebugzeroObservation(Observation):
+    """Observation from the DebugZero environment following sandbox execution."""
+
+    role_next: str = Field(default="proposer", description="The role supposed to play next")
+    current_code: str = Field(default="", description="The current state of the python code")
+    execution_result: str = Field(default="", description="Result of evaluating tests in the sandbox")
+    tests_passed: bool = Field(default=False, description="Whether the tests passed")
+    syntax_error: bool = Field(default=False, description="Whether the code had a parse/syntax error")
+
+class DebugzeroState(State):
+    """State for the DebugZero environment, extending default state with seed context."""
+    seed_id: str = Field(default="", description="ID of the HumanEval function")
+    original_code: str = Field(default="", description="Original clean seed code")
+    current_code: str = Field(default="", description="Current code after Proposer/Solver turn")
+    role_turn: str = Field(default="proposer", description="Current turn's role")

notebooks/train_colab.ipynb

@@ -2,216 +2,201 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "title",
+   "id": "f70926e8",
    "metadata": {},
    "source": [
-    "# DebugZero OpenEnv GRPO Training Notebook\n",
+    "# DebugZero Training-First Workflow (Standalone)\n",
     "\n",
-    "This notebook trains against the actual DebugZero OpenEnv server. It installs the environment from GitHub, starts or connects to the OpenEnv API, smoke-tests `reset` / `step`, then uses TRL GRPO with an optional Unsloth fast path.\n",
-    "\n",
-    "The important bit: rewards are computed by calling the live environment for model completions, not by scoring a static offline dataset."
+    "This notebook contains the complete `DebugZero` project inline. It includes the environment, executor, bug bank, seed bank, and the TRL GRPO training loop. You can run this sequentially in Colab or Kaggle."
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "checklist",
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "91a5f476",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## What This Notebook Produces\n",
-    "\n",
-    "- A live OpenEnv smoke test.\n",
-    "- Baseline reward measurements.\n",
-    "- A short GRPO run using environment rewards.\n",
-    "- Trained-vs-baseline reward comparison.\n",
-    "- Plot files under `results/` for the README or presentation."
+    "!pip install -q trl transformers datasets openenv-core[core] pydantic thefuzz matplotlib bitsandbytes unsloth"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "config",
+   "id": "5a22b902",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Repository and environment configuration.\n",
-    "REPO_URL = \"https://github.com/Ray-0906/DebugZero.git\"\n",
-    "BRANCH = \"main\"\n",
-    "\n",
-    "# Leave blank to start a local OpenEnv server inside Colab.\n",
-    "# Or set to a deployed Space URL, e.g. \"https://the-fool-09-debugzero.hf.space\".\n",
-    "# A browser page URL like \"https://huggingface.co/spaces/The-Fool-09/debugZero\" is also accepted and normalized.\n",
-    "REMOTE_OPENENV_URL = \"\"\n",
-    "\n",
-    "# Training knobs. Keep these small for hackathon proof runs; increase for final evidence.\n",
-    "USE_UNSLOTH = True\n",
-    "MODEL_ID = \"unsloth/Qwen2.5-Coder-1.5B-Instruct\"\n",
-    "FALLBACK_MODEL_ID = \"Qwen/Qwen2.5-Coder-1.5B-Instruct\"\n",
-    "OUTPUT_DIR = \"debugzero-openenv-grpo\"\n",
-    "MAX_STEPS = 30\n",
-    "NUM_GENERATIONS = 2\n",
-    "DATASET_ROUNDS = 8\n",
-    "EVAL_SAMPLES = 4\n",
-    "RUN_TRAINING = True\n",
-    "RUN_BASELINE_EVAL = True"
+    "# 1. Models\n",
+    "from openenv.core.env_server.types import Action, Observation, State\n",
+    "from pydantic import Field\n",
+    "\n",
+    "class DebugzeroAction(Action):\n",
+    "    role: str = Field(..., description=\"Role taking action: 'proposer' or 'solver'\")\n",
+    "    code: str = Field(..., description=\"Code injected (by proposer) or fixed (by solver)\")\n",
+    "\n",
+    "class DebugzeroObservation(Observation):\n",
+    "    role_next: str = Field(default=\"proposer\", description=\"The role supposed to play next\")\n",
+    "    current_code: str = Field(default=\"\", description=\"The current state of the python code\")\n",
+    "    execution_result: str = Field(default=\"\", description=\"Result of evaluating tests\")\n",
+    "    tests_passed: bool = Field(default=False, description=\"Whether the tests passed\")\n",
+    "    syntax_error: bool = Field(default=False, description=\"Whether the code had a parse/syntax error\")\n",
+    "\n",
+    "class DebugzeroState(State):\n",
+    "    seed_id: str = Field(default=\"\", description=\"ID of the HumanEval function\")\n",
+    "    original_code: str = Field(default=\"\", description=\"Original clean seed code\")\n",
+    "    current_code: str = Field(default=\"\", description=\"Current code after turn\")\n",
+    "    role_turn: str = Field(default=\"proposer\", description=\"Current turn's role\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "install",
+   "id": "d0564e89",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import importlib.util\n",
-    "import subprocess\n",
-    "import sys\n",
-    "\n",
-    "\n",
-    "def pip_install(*packages):\n",
-    "    print(\"Installing:\", \" \".join(packages))\n",
-    "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", *packages])\n",
-    "\n",
-    "# Install runtime/training packages explicitly. The repo is installed with --no-deps\n",
-    "# so its experimental training pins cannot force incompatible Colab versions.\n",
-    "pip_install(\n",
-    "    \"openenv-core[core]>=0.2.1\",\n",
-    "    \"datasets>=2.20.0\",\n",
-    "    \"trl>=0.20.0\",\n",
-    "    \"transformers>=4.51.0\",\n",
-    "    \"accelerate>=0.34.0\",\n",
-    "    \"peft>=0.12.0\",\n",
-    "    \"bitsandbytes>=0.43.0\",\n",
-    "    \"matplotlib>=3.8.0\",\n",
-    "    \"pandas>=2.0.0\",\n",
-    "    \"thefuzz[speedup]>=0.22.1\",\n",
-    "    \"uvicorn[standard]>=0.30.0\",\n",
-    "    \"requests>=2.31.0\",\n",
+    "# 2. Seed Bank\n",
+    "from dataclasses import dataclass\n",
+    "\n",
+    "@dataclass(frozen=True)\n",
+    "class SeedSpec:\n",
+    "    seed_id: str\n",
+    "    entrypoint: str\n",
+    "    prompt: str\n",
+    "    canonical_solution: str\n",
+    "    test: str\n",
+    "\n",
+    "    @property\n",
+    "    def original_code(self) -> str:\n",
+    "        return f\"{self.prompt}\\n{self.canonical_solution}\"\n",
+    "\n",
+    "SEED_BANK = (\n",
+    "    SeedSpec(\"HumanEval/0\", \"has_close_elements\", \"def has_close_elements(numbers: list[float], threshold: float) -> bool:\", \"    for idx, elem in enumerate(numbers):\\n        for idx2, elem2 in enumerate(numbers):\\n            if idx != idx2:\\n                distance = abs(elem - elem2)\\n                if distance < threshold:\\n                    return True\\n    return False\\n\", \"def check(candidate):\\n    assert candidate([1.0, 2.0, 3.0], 0.5) is False\\n    assert candidate([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3) is True\\n\\ncheck(has_close_elements)\\n\"),\n",
+    "    SeedSpec(\"DebugZero/1\", \"sum_to_n\", \"def sum_to_n(n: int) -> int:\", \"    total = 0\\n    for value in range(n + 1):\\n        total += value\\n    return total\\n\", \"def check(candidate):\\n    assert candidate(0) == 0\\n    assert candidate(1) == 1\\n    assert candidate(5) == 15\\n    assert candidate(10) == 55\\n\\ncheck(sum_to_n)\\n\"),\n",
+    "    SeedSpec(\"DebugZero/2\", \"middle_slice\", \"def middle_slice(values: list[int]) -> list[int]:\", \"    if len(values) <= 2:\\n        return []\\n    return values[1:-1]\\n\", \"def check(candidate):\\n    assert candidate([1]) == []\\n    assert candidate([1, 2]) == []\\n    assert candidate([1, 2, 3]) == [2]\\n    assert candidate([1, 2, 3, 4, 5]) == [2, 3, 4]\\n\\ncheck(middle_slice)\\n\"),\n",
+    "    SeedSpec(\"DebugZero/3\", \"is_non_decreasing\", \"def is_non_decreasing(values: list[int]) -> bool:\", \"    return all(values[idx] <= values[idx + 1] for idx in range(len(values) - 1))\\n\", \"def check(candidate):\\n    assert candidate([]) is True\\n    assert candidate([5]) is True\\n    assert candidate([1, 2, 2, 3]) is True\\n    assert candidate([3, 2]) is False\\n    assert candidate([1, 3, 2, 4]) is False\\n\\ncheck(is_non_decreasing)\\n\"),\n",
+    "    SeedSpec(\"DebugZero/4\", \"count_nonempty\", \"def count_nonempty(strings: list[str]) -> int:\", \"    total = 0\\n    for text in strings:\\n        if len(text) > 0:\\n            total += 1\\n    return total\\n\", \"def check(candidate):\\n    assert candidate([]) == 0\\n    assert candidate(['', '']) == 0\\n    assert candidate(['a', '', 'bc', '']) == 2\\n    assert candidate(['hi', 'there']) == 2\\n\\ncheck(count_nonempty)\\n\"),\n",
+    "    SeedSpec(\"DebugZero/5\", \"running_max\", \"def running_max(values: list[int]) -> int:\", \"    best = values[0]\\n    for idx in range(1, len(values)):\\n        if values[idx] > best:\\n            best = values[idx]\\n    return best\\n\", \"def check(candidate):\\n    assert candidate([3]) == 3\\n    assert candidate([3, 1, 5, 2]) == 5\\n    assert candidate([-1, -4, -2]) == -1\\n    assert candidate([0, 0, 0]) == 0\\n\\ncheck(running_max)\\n\"),\n",
     ")\n",
-    "\n",
-    "if USE_UNSLOTH:\n",
-    "    try:\n",
-    "        pip_install(\"unsloth\")\n",
-    "    except Exception as exc:\n",
-    "        print(\"Unsloth install failed; the notebook will use native TRL fallback.\")\n",
-    "        print(exc)\n",
-    "\n",
-    "pip_install(\"--no-deps\", f\"git+{REPO_URL}@{BRANCH}\")"
+    "SEED_BY_ID = {seed.seed_id: seed for seed in SEED_BANK}\n",
+    "def get_seed_by_id(seed_id: str) -> SeedSpec: return SEED_BY_ID[seed_id]"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "server",
+   "id": "0ed909d0",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import atexit\n",
+    "# 3. Executor\n",
     "import os\n",
-    "import re\n",
     "import subprocess\n",
     "import sys\n",
-    "import time\n",
-    "from urllib.parse import urlparse\n",
-    "\n",
-    "import requests\n",
-    "\n",
-    "\n",
-    "def normalize_space_url(url: str) -> str:\n",
-    "    url = (url or \"\").strip().rstrip(\"/\")\n",
-    "    if not url:\n",
-    "        return \"\"\n",
-    "    parsed = urlparse(url)\n",
-    "    if parsed.netloc == \"huggingface.co\" and parsed.path.startswith(\"/spaces/\"):\n",
-    "        parts = parsed.path.strip(\"/\").split(\"/\")\n",
-    "        if len(parts) >= 3:\n",
-    "            owner, space = parts[1], parts[2]\n",
-    "            return f\"https://{owner}-{space}.hf.space\".lower()\n",
-    "    return url\n",
-    "\n",
-    "REMOTE_OPENENV_URL = normalize_space_url(REMOTE_OPENENV_URL)\n",
-    "\n",
-    "if REMOTE_OPENENV_URL:\n",
-    "    BASE_URL = REMOTE_OPENENV_URL\n",
-    "    server_process = None\n",
-    "else:\n",
-    "    BASE_URL = \"http://127.0.0.1:8000\"\n",
-    "    server_process = subprocess.Popen(\n",
-    "        [sys.executable, \"-m\", \"debugZero.server.app\", \"--host\", \"127.0.0.1\", \"--port\", \"8000\"],\n",
-    "        stdout=subprocess.PIPE,\n",
-    "        stderr=subprocess.STDOUT,\n",
-    "        text=True,\n",
-    "    )\n",
-    "    atexit.register(lambda: server_process and server_process.poll() is None and server_process.terminate())\n",
-    "\n",
-    "\n",
-    "def wait_for_openenv(base_url, timeout_s=120):\n",
-    "    deadline = time.time() + timeout_s\n",
-    "    last_error = None\n",
-    "    while time.time() < deadline:\n",
+    "import tempfile\n",
+    "import ast\n",
+    "\n",
+    "BLOCKED_IMPORTS = [\"os\", \"sys\", \"subprocess\", \"shutil\", \"pathlib\"]\n",
+    "BLOCKED_BUILTINS = [\"__import__\", \"eval\", \"exec\", \"open\"]\n",
+    "\n",
+    "def is_safe(code: str) -> bool:\n",
+    "    for mod in BLOCKED_IMPORTS:\n",
+    "        if f\"import {mod}\" in code or f\"from {mod}\" in code:\n",
+    "            return False\n",
+    "    for b in BLOCKED_BUILTINS:\n",
+    "        if b in code: return False\n",
+    "    try:\n",
+    "        tree = ast.parse(code)\n",
+    "    except SyntaxError:\n",
+    "        return False\n",
+    "    for node in ast.walk(tree):\n",
+    "        if isinstance(node, ast.Import):\n",
+    "            for alias in node.names:\n",
+    "                if alias.name.split('.')[0] in BLOCKED_IMPORTS: return False\n",
+    "        elif isinstance(node, ast.ImportFrom):\n",
+    "            if node.module and node.module.split('.')[0] in BLOCKED_IMPORTS: return False\n",
+    "        elif isinstance(node, ast.Call):\n",
+    "            if isinstance(node.func, ast.Name) and node.func.id in BLOCKED_BUILTINS: return False\n",
+    "    return True\n",
+    "\n",
+    "class ExecutionResult:\n",
+    "    def __init__(self, passed: bool, output: str, syntax_error: bool = False, timeout_error: bool = False):\n",
+    "        self.passed = passed\n",
+    "        self.output = output\n",
+    "        self.syntax_error = syntax_error\n",
+    "        self.timeout_error = timeout_error\n",
+    "\n",
+    "def execute_code(code: str, tests: str, timeout: int = 5) -> ExecutionResult:\n",
+    "    full_code = f\"{code}\\n\\n{tests}\"\n",
+    "    if not is_safe(full_code):\n",
+    "        try:\n",
+    "            ast.parse(full_code)\n",
+    "            return ExecutionResult(passed=False, output=\"Unsafe import detected.\", syntax_error=False)\n",
+    "        except SyntaxError as e:\n",
+    "            return ExecutionResult(passed=False, output=f\"SyntaxError: {e}\", syntax_error=True)\n",
+    "            \n",
+    "    with tempfile.TemporaryDirectory() as temp_dir:\n",
+    "        temp_file = os.path.join(temp_dir, \"exec_script.py\")\n",
+    "        with open(temp_file, \"w\") as f: f.write(full_code)\n",
     "        try:\n",
-    "            response = requests.get(f\"{base_url}/schema\", timeout=5)\n",
-    "            if response.status_code == 200:\n",
-    "                return response.json()\n",
-    "            last_error = f\"HTTP {response.status_code}: {response.text[:200]}\"\n",
-    "        except Exception as exc:\n",
-    "            last_error = exc\n",
-    "        time.sleep(2)\n",
-    "\n",
-    "    if server_process and server_process.stdout:\n",
-    "        print(\"--- OpenEnv server output ---\")\n",
-    "        print(server_process.stdout.read())\n",
-    "    raise RuntimeError(f\"OpenEnv server did not become ready at {base_url}: {last_error}\")\n",
-    "\n",
-    "schema = wait_for_openenv(BASE_URL)\n",
-    "print(\"Connected to OpenEnv:\", BASE_URL)\n",
-    "schema"
+    "            result = subprocess.run([sys.executable, temp_file], capture_output=True, text=True, timeout=timeout)\n",
+    "            if result.returncode == 0:\n",
+    "                return ExecutionResult(passed=True, output=result.stdout)\n",
+    "            else:\n",
+    "                syntax_error = \"SyntaxError\" in result.stderr\n",
+    "                return ExecutionResult(passed=False, output=result.stderr, syntax_error=syntax_error)\n",
+    "        except subprocess.TimeoutExpired:\n",
+    "            return ExecutionResult(passed=False, output=\"Execution timed out.\", timeout_error=True)\n",
+    "        except Exception as e:\n",
+    "            return ExecutionResult(passed=False, output=str(e))"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "client-smoke-test",
+   "id": "0f7263c1",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from debugZero.client import DebugzeroEnv\n",
-    "from debugZero.models import DebugzeroAction\n",
+    "# 4. Plausibility Score\n",
+    "from thefuzz import fuzz\n",
     "\n",
-    "\n",
-    "def observation(result):\n",
-    "    return getattr(result, \"observation\", result)\n",
-    "\n",
-    "with DebugzeroEnv(base_url=BASE_URL) as env:\n",
-    "    reset_obs = observation(env.reset())\n",
-    "    print(\"Initial role:\", reset_obs.role_next)\n",
-    "    print(reset_obs.current_code[:300])\n",
-    "\n",
-    "    # This exact boundary bug should fail after the equality-at-threshold grader fix.\n",
-    "    buggy_code = reset_obs.current_code.replace(\"distance < threshold\", \"distance <= threshold\")\n",
-    "    prop_obs = observation(env.step(DebugzeroAction(role=\"proposer\", code=buggy_code)))\n",
-    "    print(\"After proposer:\", {\"role_next\": prop_obs.role_next, \"tests_passed\": prop_obs.tests_passed, \"syntax_error\": prop_obs.syntax_error})\n",
-    "    assert prop_obs.tests_passed is False and prop_obs.syntax_error is False, \"Boundary bug was not caught by environment tests.\"\n",
-    "\n",
-    "    solve_obs = observation(env.step(DebugzeroAction(role=\"solver\", code=reset_obs.current_code)))\n",
-    "    print(\"After solver:\", {\"role_next\": solve_obs.role_next, \"tests_passed\": solve_obs.tests_passed, \"syntax_error\": solve_obs.syntax_error})\n",
-    "    assert solve_obs.tests_passed is True and solve_obs.syntax_error is False, \"Clean solution should pass.\""
+    "def compute_ast_distance(original_code: str, mutated_code: str) -> float:\n",
+    "    try:\n",
+    "        orig_ast = ast.dump(ast.parse(original_code))\n",
+    "        mut_ast = ast.dump(ast.parse(mutated_code))\n",
+    "    except SyntaxError:\n",
+    "        return 0.0\n",
+    "    ratio = fuzz.ratio(orig_ast, mut_ast)\n",
+    "    if 85 <= ratio: return 1.0 \n",
+    "    elif 50 <= ratio < 85: return max(0.1, (ratio - 50) / 35.0)\n",
+    "    else: return 0.0"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "dataset-and-prompts",
+   "id": "10a37cca",
    "metadata": {},
    "outputs": [],
    "source": [
+    "# 5. Prompts and Summarization (Dual Role Sampler)\n",
     "import re\n",
-    "from datasets import Dataset\n",
     "\n",
-    "PROPOSER_PROMPT = \"\"\"You are the Proposer in DebugZero.\n",
-    "Inject one realistic, syntax-valid bug into the clean Python function.\n",
-    "The mutated function must run but fail at least one environment test.\n",
-    "Prefer small human-like edits: boundary, comparison, operator, return-condition, or loop mistakes.\n",
-    "Return only the complete modified Python code inside a python code fence.\n",
+    "PROPOSER_PROMPT = \"\"\"You are the Proposer in a debugging self-play game.\n",
+    "Given a clean Python function, inject a realistic logical bug into it.\n",
+    "Rules:\n",
+    "- Make exactly one small logical change.\n",
+    "- Keep the code valid Python.\n",
+    "- Keep the same function signature.\n",
+    "- Preserve the overall structure and formatting as much as possible.\n",
+    "- Prefer one of these mutation families: off_by_one, wrong_operator, wrong_builtin,\n",
+    "  condition_negation, loop_boundary_shift, or slice_boundary_corruption.\n",
+    "- Aim for an edge-case behavior change, not a cosmetic refactor.\n",
+    "- Avoid helper extraction, renaming-only edits, comment-only changes, or multi-line rewrites.\n",
+    "- Return only the full modified Python code inside triple backticks.\n",
+    "{focus_instruction}\n",
     "\n",
     "Clean function:\n",
     "```python\n",
@@ -219,390 +204,622 @@
     "```\n",
     "\"\"\"\n",
     "\n",
-    "SOLVER_PROMPT = \"\"\"You are the Solver in DebugZero.\n",
-    "Repair the buggy Python function so it passes the environment tests.\n",
-    "Return only the complete repaired Python code inside a python code fence.\n",
+    "SOLVER_PROMPT_CONCISE = \"\"\"You are the Solver in a debugging self-play game.\n",
+    "Fix the bug with the smallest correct local change and return only the full fixed Python code inside triple backticks.\n",
     "\n",
     "Buggy function:\n",
     "```python\n",
     "{code}\n",
     "```\n",
-    "\"\"\"\n",
-    "\n",
-    "\n",
-    "def extract_code(text):\n",
-    "    if isinstance(text, list):\n",
-    "        if text and isinstance(text[0], dict):\n",
-    "            text = text[0].get(\"content\", \"\")\n",
-    "        else:\n",
-    "            text = \"\\n\".join(map(str, text))\n",
-    "    text = str(text or \"\")\n",
-    "    match = re.search(r\"```(?:python)?\\s*(.*?)```\", text, flags=re.DOTALL | re.IGNORECASE)\n",
-    "    return (match.group(1) if match else text).strip()\n",
-    "\n",
-    "\n",
-    "def deterministic_bug(clean_code):\n",
-    "    replacements = [\n",
-    "        (\"distance < threshold\", \"distance <= threshold\"),\n",
-    "        (\"idx != idx2\", \"idx == idx2\"),\n",
-    "        (\"return True\", \"return False\"),\n",
-    "        (\"return False\", \"return True\"),\n",
-    "    ]\n",
-    "    for old, new in replacements:\n",
-    "        if old in clean_code:\n",
-    "            return clean_code.replace(old, new, 1)\n",
-    "    return clean_code + \"\\n# BUG: intentionally left for solver\\n\"\n",
     "\n",
+    "Failure summary:\n",
+    "{execution_result}\n",
+    "\"\"\"\n",
     "\n",
-    "def build_openenv_dataset(num_rounds=8):\n",
-    "    rows = []\n",
-    "    with DebugzeroEnv(base_url=BASE_URL) as env:\n",
-    "        for episode in range(num_rounds):\n",
-    "            clean_obs = observation(env.reset())\n",
-    "            clean_code = clean_obs.current_code\n",
-    "            buggy_code = deterministic_bug(clean_code)\n",
-    "\n",
-    "            rows.append({\n",
-    "                \"prompt\": PROPOSER_PROMPT.format(code=clean_code),\n",
-    "                \"role\": \"proposer\",\n",
-    "                \"clean_code\": clean_code,\n",
-    "                \"buggy_code\": \"\",\n",
-    "                \"episode\": episode,\n",
-    "            })\n",
-    "            rows.append({\n",
-    "                \"prompt\": SOLVER_PROMPT.format(code=buggy_code),\n",
-    "                \"role\": \"solver\",\n",
-    "                \"clean_code\": clean_code,\n",
-    "                \"buggy_code\": buggy_code,\n",
-    "                \"episode\": episode,\n",
-    "            })\n",
-    "    return Dataset.from_list(rows)\n",
-    "\n",
-    "train_dataset = build_openenv_dataset(num_rounds=DATASET_ROUNDS)\n",
-    "print(train_dataset)\n",
-    "print(train_dataset[0][\"prompt\"][:500])"
+    "TRACEBACK_HINTS = (\"Traceback\", \"AssertionError\", \"SyntaxError\", \"TypeError\", \"NameError\", \"ValueError\", \"IndexError\", \"KeyError\", \"ZeroDivisionError\", \"RuntimeError\", \"Timeout\",)\n",
+    "\n",
+    "def _truncate_text(text: str, max_chars: int) -> str:\n",
+    "    cleaned = re.sub(r\"[ \\t]+\", \" \", text.strip())\n",
+    "    if len(cleaned) <= max_chars: return cleaned\n",
+    "    return cleaned[: max(0, max_chars - 3)].rstrip() + \"...\"\n",
+    "\n",
+    "def summarize_failure_output(execution_result: str, *, max_lines: int = 3, max_chars: int = 220) -> str:\n",
+    "    text = execution_result.strip()\n",
+    "    if not text: return \"No failure output provided.\"\n",
+    "    if text in {\"Unsafe import detected.\", \"Execution timed out.\"} or text.startswith(\"SyntaxError:\"):\n",
+    "        return _truncate_text(text, max_chars)\n",
+    "    lines = [line.strip() for line in text.splitlines() if line.strip()]\n",
+    "    if not lines: return \"No failure output provided.\"\n",
+    "    traceback_positions = [idx for idx, line in enumerate(lines) if \"Traceback\" in line]\n",
+    "    if traceback_positions:\n",
+    "        tail = lines[traceback_positions[-1] :]\n",
+    "        if len(tail) > max_lines: lines = [tail[0], *tail[-(max_lines - 1) :]]\n",
+    "        else: lines = tail\n",
+    "    else:\n",
+    "        interesting_lines = [line for line in lines if any(hint in line for hint in TRACEBACK_HINTS)]\n",
+    "        if interesting_lines: lines = interesting_lines[-max_lines:]\n",
+    "        else: lines = lines[-max_lines:]\n",
+    "    return _truncate_text(\"\\n\".join(lines), max_chars)\n",
+    "\n",
+    "def sample_proposer_prompt(code: str, bug_focus: str | None = None) -> str:\n",
+    "    focus_instruction = \"\"\n",
+    "    if bug_focus:\n",
+    "        focus_instruction = f\"- Focus specifically on the `{bug_focus}` mutation family.\\n- Keep the edit local so the bug can be repaired with a small fix.\"\n",
+    "    return PROPOSER_PROMPT.format(code=code, focus_instruction=focus_instruction)\n",
+    "\n",
+    "def sample_solver_prompt(code: str, execution_result: str = \"\", *, mode: str = \"concise\") -> str:\n",
+    "    failure_output = summarize_failure_output(execution_result)\n",
+    "    return SOLVER_PROMPT_CONCISE.format(code=code, execution_result=failure_output)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "reward",
+   "id": "db97c7b9",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def proposer_reward(obs, submitted_code, clean_code):\n",
-    "    if obs.syntax_error:\n",
-    "        return -1.0\n",
-    "    if submitted_code.strip() == clean_code.strip():\n",
-    "        return -0.5\n",
-    "    return 1.0 if not obs.tests_passed else 0.0\n",
-    "\n",
-    "\n",
-    "def solver_reward(obs, submitted_code, clean_code):\n",
-    "    if obs.syntax_error:\n",
-    "        return -1.0\n",
-    "    if submitted_code.strip() == clean_code.strip() and obs.tests_passed:\n",
-    "        return 1.25\n",
-    "    return 1.0 if obs.tests_passed else 0.0\n",
-    "\n",
-    "\n",
-    "def _column(kwargs, singular, plural=None):\n",
-    "    if singular in kwargs and kwargs[singular] is not None:\n",
-    "        return kwargs[singular]\n",
-    "    if plural and plural in kwargs and kwargs[plural] is not None:\n",
-    "        return kwargs[plural]\n",
-    "    raise KeyError(f\"Reward function missing dataset column '{singular}'. Available keys: {sorted(kwargs.keys())}\")\n",
-    "\n",
-    "\n",
-    "def openenv_reward(*args, **kwargs):\n",
-    "    # TRL GRPO custom rewards receive prompts, completions, and all extra dataset columns.\n",
-    "    # Across versions this may arrive as positional args or keyword args, so parse defensively.\n",
-    "    completions = kwargs.get(\"completions\")\n",
-    "    if completions is None:\n",
-    "        if len(args) >= 2:\n",
-    "            completions = args[1]\n",
-    "        elif len(args) == 1:\n",
-    "            completions = args[0]\n",
-    "        else:\n",
-    "            raise TypeError(\"Reward function did not receive completions.\")\n",
-    "\n",
-    "    roles = _column(kwargs, \"role\", \"roles\")\n",
-    "    clean_codes = _column(kwargs, \"clean_code\", \"clean_codes\")\n",
-    "    buggy_codes = kwargs.get(\"buggy_code\", kwargs.get(\"buggy_codes\", [\"\"] * len(completions)))\n",
-    "\n",
-    "    rewards = []\n",
-    "    with DebugzeroEnv(base_url=BASE_URL) as env:\n",
-    "        for completion, sample_role, clean, bug in zip(completions, roles, clean_codes, buggy_codes):\n",
-    "            code = extract_code(completion)\n",
-    "            env.reset()\n",
-    "            if sample_role == \"proposer\":\n",
-    "                obs = observation(env.step(DebugzeroAction(role=\"proposer\", code=code)))\n",
-    "                rewards.append(proposer_reward(obs, code, clean))\n",
-    "            elif sample_role == \"solver\":\n",
-    "                env.step(DebugzeroAction(role=\"proposer\", code=bug))\n",
-    "                obs = observation(env.step(DebugzeroAction(role=\"solver\", code=code)))\n",
-    "                rewards.append(solver_reward(obs, code, clean))\n",
-    "            else:\n",
-    "                rewards.append(0.0)\n",
-    "    return rewards\n",
-    "\n",
-    "# Reward sanity checks against the live environment.\n",
-    "clean = train_dataset[0][\"clean_code\"]\n",
-    "buggy = deterministic_bug(clean)\n",
-    "print(\"proposer boundary bug reward:\", openenv_reward([None], [f\"```python\\n{buggy}\\n```\"], role=[\"proposer\"], clean_code=[clean], buggy_code=[\"\"]))\n",
-    "print(\"solver clean repair reward:\", openenv_reward([None], [f\"```python\\n{clean}\\n```\"], role=[\"solver\"], clean_code=[clean], buggy_code=[buggy]))"
+    "# 6. Bug Operations & Injector\n",
+    "import random\n",
+    "import copy\n",
+    "\n",
+    "BUILTIN_PAIRS = {\"min\": \"max\", \"max\": \"min\", \"any\": \"all\", \"all\": \"any\", \"sum\": \"len\", \"len\": \"sum\"}\n",
+    "\n",
+    "def is_safe_injection(code: str) -> bool:\n",
+    "    for blocked in BLOCKED_IMPORTS:\n",
+    "        if f\"import {blocked}\" in code or f\"from {blocked}\" in code:\n",
+    "            return False\n",
+    "    return True\n",
+    "\n",
+    "class BugInjectorVisitor(ast.NodeTransformer):\n",
+    "    def __init__(self, target_operator: str):\n",
+    "        super().__init__()\n",
+    "        self.target_operator = target_operator\n",
+    "        self.mutated = False\n",
+    "\n",
+    "    def visit_Constant(self, node):\n",
+    "        self.generic_visit(node)\n",
+    "        if self.mutated: return node\n",
+    "        if self.target_operator == \"off_by_one\" and isinstance(node.value, int) and not isinstance(node.value, bool):\n",
+    "            node.value += random.choice([-1, 1])\n",
+    "            self.mutated = True\n",
+    "        return node\n",
+    "\n",
+    "    def visit_Compare(self, node):\n",
+    "        self.generic_visit(node)\n",
+    "        if self.mutated: return node\n",
+    "        if self.target_operator == \"wrong_operator\":\n",
+    "            if isinstance(node.ops[0], ast.Lt):\n",
+    "                node.ops[0] = ast.GtE()\n",
+    "                self.mutated = True\n",
+    "            elif isinstance(node.ops[0], ast.LtE):\n",
+    "                node.ops[0] = ast.Gt()\n",
+    "                self.mutated = True\n",
+    "            elif isinstance(node.ops[0], ast.Gt):\n",
+    "                node.ops[0] = ast.LtE()\n",
+    "                self.mutated = True\n",
+    "            elif isinstance(node.ops[0], ast.GtE):\n",
+    "                node.ops[0] = ast.Lt()\n",
+    "                self.mutated = True\n",
+    "            elif isinstance(node.ops[0], ast.Eq):\n",
+    "                node.ops[0] = ast.NotEq()\n",
+    "                self.mutated = True\n",
+    "            elif isinstance(node.ops[0], ast.NotEq):\n",
+    "                node.ops[0] = ast.Eq()\n",
+    "                self.mutated = True\n",
+    "        return node\n",
+    "\n",
+    "    def visit_BinOp(self, node):\n",
+    "        self.generic_visit(node)\n",
+    "        if self.mutated: return node\n",
+    "        if self.target_operator == \"wrong_operator\":\n",
+    "            if isinstance(node.op, ast.Add):\n",
+    "                node.op = ast.Sub()\n",
+    "                self.mutated = True\n",
+    "            elif isinstance(node.op, ast.Sub):\n",
+    "                node.op = ast.Add()\n",
+    "                self.mutated = True\n",
+    "            elif isinstance(node.op, ast.Mult):\n",
+    "                node.op = ast.FloorDiv()\n",
+    "                self.mutated = True\n",
+    "            elif isinstance(node.op, ast.Div):\n",
+    "                node.op = ast.Mult()\n",
+    "                self.mutated = True\n",
+    "        return node\n",
+    "        \n",
+    "    def visit_Call(self, node):\n",
+    "        self.generic_visit(node)\n",
+    "        if self.mutated: return node\n",
+    "        if isinstance(node.func, ast.Name):\n",
+    "            if self.target_operator == \"wrong_builtin\" and node.func.id in BUILTIN_PAIRS:\n",
+    "                node.func.id = BUILTIN_PAIRS[node.func.id]\n",
+    "                self.mutated = True\n",
+    "            elif self.target_operator == \"loop_boundary_shift\" and node.func.id == \"range\":\n",
+    "                if len(node.args) == 1:\n",
+    "                    node.args[0] = ast.BinOp(left=node.args[0], op=ast.Add(), right=ast.Constant(value=1))\n",
+    "                    self.mutated = True\n",
+    "                elif len(node.args) == 2:\n",
+    "                    node.args[0] = ast.BinOp(left=node.args[0], op=ast.Sub(), right=ast.Constant(value=1))\n",
+    "                    self.mutated = True\n",
+    "        return node\n",
+    "\n",
+    "    def visit_If(self, node):\n",
+    "        self.generic_visit(node)\n",
+    "        if self.mutated: return node\n",
+    "        if self.target_operator == \"condition_negation\":\n",
+    "            node.test = ast.UnaryOp(op=ast.Not(), operand=node.test)\n",
+    "            self.mutated = True\n",
+    "        if self.target_operator == \"missing_base_case\":\n",
+    "            for idx, child in enumerate(node.body):\n",
+    "                if isinstance(child, ast.Return):\n",
+    "                    node.body[idx] = ast.Pass()\n",
+    "                    self.mutated = True\n",
+    "                    break\n",
+    "        return node\n",
+    "        \n",
+    "    def visit_Slice(self, node):\n",
+    "        self.generic_visit(node)\n",
+    "        if self.mutated: return node\n",
+    "        if self.target_operator == \"slice_boundary_corruption\":\n",
+    "            if node.lower is not None:\n",
+    "                node.lower = ast.BinOp(left=node.lower, op=ast.Add(), right=ast.Constant(value=1))\n",
+    "                self.mutated = True\n",
+    "            elif node.upper is not None:\n",
+    "                node.upper = ast.BinOp(left=node.upper, op=ast.Sub(), right=ast.Constant(value=1))\n",
+    "                self.mutated = True\n",
+    "        return node\n",
+    "        \n",
+    "    def visit_Assign(self, node):\n",
+    "        self.generic_visit(node)\n",
+    "        if self.mutated: return node\n",
+    "        if self.target_operator == \"variable_swap\" and getattr(node, \"targets\", None):\n",
+    "            if isinstance(node.targets[0], ast.Tuple) and len(node.targets[0].elts) >= 2:\n",
+    "                node.targets[0].elts[0], node.targets[0].elts[1] = node.targets[0].elts[1], node.targets[0].elts[0]\n",
+    "                self.mutated = True\n",
+    "        return node\n",
+    "\n",
+    "def inject_bug(original_code: str, proposed_operator: str) -> tuple[str, bool]:\n",
+    "    try: tree = ast.parse(original_code)\n",
+    "    except SyntaxError: return original_code, False\n",
+    "    injector = BugInjectorVisitor(proposed_operator)\n",
+    "    mutated_tree = injector.visit(copy.deepcopy(tree))\n",
+    "    ast.fix_missing_locations(mutated_tree)\n",
+    "    mutated_code = ast.unparse(mutated_tree)\n",
+    "    if mutated_code.strip() == original_code.strip(): return original_code, False\n",
+    "    if not is_safe_injection(mutated_code): return original_code, False\n",
+    "    try: ast.parse(mutated_code)\n",
+    "    except SyntaxError: return original_code, False\n",
+    "    return mutated_code, True\n",
+    "\n",
+    "def infer_bug_operator(original_code: str, candidate_code: str) -> str | None:\n",
+    "    try:\n",
+    "        original_tree = ast.parse(original_code)\n",
+    "        candidate_tree = ast.parse(candidate_code)\n",
+    "    except SyntaxError:\n",
+    "        return None\n",
+    "    if ast.dump(original_tree) == ast.dump(candidate_tree):\n",
+    "        return None\n",
+    "    return \"unknown\" # simplified for inline layout logic"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "model",
+   "id": "73ba2c61",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import torch\n",
-    "\n",
-    "HAS_UNSLOTH = False\n",
-    "if USE_UNSLOTH:\n",
-    "    try:\n",
-    "        from unsloth import FastLanguageModel, PatchFastRL, is_bfloat16_supported\n",
-    "        PatchFastRL(\"GRPO\", FastLanguageModel)\n",
-    "        HAS_UNSLOTH = True\n",
-    "    except Exception as exc:\n",
-    "        print(\"Using native Transformers/TRL fallback because Unsloth is unavailable:\")\n",
-    "        print(exc)\n",
-    "        HAS_UNSLOTH = False\n",
-    "\n",
-    "if not HAS_UNSLOTH:\n",
-    "    is_bfloat16_supported = lambda: False\n",
-    "\n",
-    "from trl import GRPOConfig, GRPOTrainer\n",
-    "\n",
-    "if HAS_UNSLOTH:\n",
-    "    model, tokenizer = FastLanguageModel.from_pretrained(\n",
-    "        model_name=MODEL_ID,\n",
-    "        max_seq_length=2048,\n",
-    "        load_in_4bit=True,\n",
-    "        fast_inference=False,\n",
-    "    )\n",
-    "    model = FastLanguageModel.get_peft_model(\n",
-    "        model,\n",
-    "        r=16,\n",
-    "        target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"],\n",
-    "        lora_alpha=16,\n",
-    "        lora_dropout=0,\n",
-    "        bias=\"none\",\n",
-    "        use_gradient_checkpointing=\"unsloth\",\n",
-    "        random_state=3407,\n",
-    "    )\n",
-    "else:\n",
-    "    from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-    "    tokenizer = AutoTokenizer.from_pretrained(FALLBACK_MODEL_ID, trust_remote_code=True)\n",
-    "    if tokenizer.pad_token is None:\n",
-    "        tokenizer.pad_token = tokenizer.eos_token\n",
-    "    model = AutoModelForCausalLM.from_pretrained(\n",
-    "        FALLBACK_MODEL_ID,\n",
-    "        torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,\n",
-    "        device_map=\"auto\" if torch.cuda.is_available() else None,\n",
-    "        trust_remote_code=True,\n",
-    "    )"
+    "# 7. Environment & Bug Bank\n",
+    "V1_BUG_OPERATORS = (\"wrong_operator\", \"wrong_builtin\", \"condition_negation\", \"off_by_one\", \"loop_boundary_shift\", \"slice_boundary_corruption\",)\n",
+    "MAX_VERIFIED_BUGS_PER_SEED = 4\n",
+    "HOLDOUT_BUGS_PER_SEED = 1\n",
+    "MAX_MUTATION_ATTEMPTS = 4\n",
+    "BUG_OPERATOR_PRIORITY = {\"loop_boundary_shift\": 6, \"slice_boundary_corruption\": 5, \"condition_negation\": 4, \"wrong_operator\": 3, \"off_by_one\": 2, \"wrong_builtin\": 1}\n",
+    "\n",
+    "@dataclass(frozen=True)\n",
+    "class BugSample:\n",
+    "    seed_id: str\n",
+    "    original_code: str\n",
+    "    buggy_code: str\n",
+    "    bug_operator: str\n",
+    "    execution_result: str\n",
+    "\n",
+    "@dataclass(frozen=True)\n",
+    "class BugBank:\n",
+    "    train_samples: tuple[BugSample, ...]\n",
+    "    eval_samples: tuple[BugSample, ...]\n",
+    "\n",
+    "def validate_seed(seed: SeedSpec) -> None:\n",
+    "    result = execute_code(seed.original_code, seed.test)\n",
+    "    if result.syntax_error or not result.passed:\n",
+    "        raise ValueError(f\"Seed {seed.seed_id} does not pass.\")\n",
+    "\n",
+    "def _count_nonempty_lines(text: str) -> int:\n",
+    "    return sum(1 for line in text.splitlines() if line.strip())\n",
+    "\n",
+    "def _bug_difficulty_score(seed: SeedSpec, sample: BugSample) -> float:\n",
+    "    operator_score = BUG_OPERATOR_PRIORITY.get(sample.bug_operator, 0)\n",
+    "    ast_similarity = compute_ast_distance(seed.original_code, sample.buggy_code)\n",
+    "    execution_lines = _count_nonempty_lines(sample.execution_result)\n",
+    "    return float(operator_score) + ast_similarity + min(execution_lines / 4.0, 1.0)\n",
+    "\n",
+    "def _collect_verified_bugs(seed: SeedSpec) -> list[BugSample]:\n",
+    "    verified_samples: list[BugSample] = []\n",
+    "    seen_codes: set[str] = set()\n",
+    "    for bug_operator in V1_BUG_OPERATORS:\n",
+    "        for attempt in range(MAX_MUTATION_ATTEMPTS):\n",
+    "            random.seed(f\"{seed.seed_id}:{bug_operator}:{attempt}\")\n",
+    "            buggy_code, changed = inject_bug(seed.original_code, bug_operator)\n",
+    "            if not changed or buggy_code in seen_codes: continue\n",
+    "            result = execute_code(buggy_code, seed.test)\n",
+    "            if result.syntax_error or result.passed: continue\n",
+    "            seen_codes.add(buggy_code)\n",
+    "            verified_samples.append(BugSample(seed.seed_id, seed.original_code, buggy_code, bug_operator, result.output[:500] if result.output else \"\"))\n",
+    "    return verified_samples\n",
+    "\n",
+    "def build_bug_bank() -> BugBank:\n",
+    "    train_samples, eval_samples = [], []\n",
+    "    for seed in SEED_BANK:\n",
+    "        validate_seed(seed)\n",
+    "        verified_samples = sorted(_collect_verified_bugs(seed), key=lambda sample: _bug_difficulty_score(seed, sample), reverse=True)\n",
+    "        if len(verified_samples) <= HOLDOUT_BUGS_PER_SEED: raise ValueError(f\"Seed {seed.seed_id} under-produced.\")\n",
+    "        eval_samples.extend(verified_samples[:HOLDOUT_BUGS_PER_SEED])\n",
+    "        train_samples.extend(verified_samples[HOLDOUT_BUGS_PER_SEED : HOLDOUT_BUGS_PER_SEED + MAX_VERIFIED_BUGS_PER_SEED])\n",
+    "    return BugBank(tuple(train_samples), tuple(eval_samples))"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "baseline",
+   "id": "2736e2d9",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def model_device(model):\n",
-    "    try:\n",
-    "        return next(model.parameters()).device\n",
-    "    except Exception:\n",
-    "        return torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "\n",
-    "\n",
-    "def generate_completion(prompt, max_new_tokens=384):\n",
-    "    inputs = tokenizer(prompt, return_tensors=\"pt\").to(model_device(model))\n",
-    "    with torch.no_grad():\n",
-    "        output = model.generate(\n",
-    "            **inputs,\n",
-    "            max_new_tokens=max_new_tokens,\n",
-    "            do_sample=True,\n",
-    "            temperature=0.7,\n",
-    "            top_p=0.9,\n",
-    "            pad_token_id=tokenizer.eos_token_id,\n",
-    "        )\n",
-    "    return tokenizer.decode(output[0][inputs[\"input_ids\"].shape[-1]:], skip_special_tokens=True)\n",
-    "\n",
-    "\n",
-    "def evaluate_policy(dataset, n=4):\n",
-    "    rows = [dataset[i] for i in range(min(n, len(dataset)))]\n",
-    "    completions = [generate_completion(row[\"prompt\"]) for row in rows]\n",
-    "    rewards = openenv_reward(\n",
-    "        [row[\"prompt\"] for row in rows],\n",
-    "        completions,\n",
-    "        role=[row[\"role\"] for row in rows],\n",
-    "        clean_code=[row[\"clean_code\"] for row in rows],\n",
-    "        buggy_code=[row[\"buggy_code\"] for row in rows],\n",
-    "    )\n",
-    "    return rewards, completions\n",
-    "\n",
-    "if RUN_BASELINE_EVAL:\n",
-    "    baseline_rewards, baseline_completions = evaluate_policy(train_dataset, n=EVAL_SAMPLES)\n",
-    "else:\n",
-    "    baseline_rewards, baseline_completions = [], []\n",
-    "\n",
-    "print(\"Baseline rewards:\", baseline_rewards)\n",
-    "if baseline_rewards:\n",
-    "    print(\"Baseline mean:\", sum(baseline_rewards) / len(baseline_rewards))"
+    "# 8. Training Rewards\n",
+    "import statistics\n",
+    "from collections import deque\n",
+    "\n",
+    "solve_rate_history: dict[str, deque[float]] = {}\n",
+    "def reset_reward_history() -> None: solve_rate_history.clear()\n",
+    "def get_solve_rate(seed_id: str) -> float: return statistics.mean(solve_rate_history[seed_id]) if solve_rate_history.get(seed_id) else 0.5\n",
+    "def record_solve_result(seed_id: str, solved: bool) -> None:\n",
+    "    if seed_id not in solve_rate_history: solve_rate_history[seed_id] = deque(maxlen=20)\n",
+    "    solve_rate_history[seed_id].append(1.0 if solved else 0.0)\n",
+    "\n",
+    "def is_effectively_unchanged(original_code: str, candidate_code: str) -> bool:\n",
+    "    try: return ast.dump(ast.parse(original_code)) == ast.dump(ast.parse(candidate_code))\n",
+    "    except SyntaxError: return original_code.strip() == candidate_code.strip()\n",
+    "\n",
+    "def compute_proposer_reward(meta: dict) -> float:\n",
+    "    if meta.get(\"syntax_error\", False) or meta.get(\"unsafe_code\", False): return -0.5\n",
+    "    if meta.get(\"unchanged_code\", False) or meta.get(\"tests_passed\", True): return 0.0\n",
+    "    if meta.get(\"changed_but_passing\", False): return -0.1\n",
+    "    plausibility_bonus = meta.get(\"plausibility_score\", 0.0)\n",
+    "    learnability_bonus = 1.0 if 0.2 <= get_solve_rate(meta[\"seed_id\"]) <= 0.8 else 0.0\n",
+    "    return 1.0 + plausibility_bonus + learnability_bonus\n",
+    "\n",
+    "def compute_solver_reward(meta: dict) -> float:\n",
+    "    solved = meta.get(\"tests_passed\", False)\n",
+    "    syntax_error = meta.get(\"syntax_error\", True)\n",
+    "    unsafe_code = meta.get(\"unsafe_code\", False)\n",
+    "    record_solve_result(meta[\"seed_id\"], solved and not syntax_error and not unsafe_code)\n",
+    "    if syntax_error or unsafe_code: return -0.5\n",
+    "    if solved: return 1.0\n",
+    "    return 0.0"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "train",
+   "id": "892a82b2",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import inspect\n",
-    "\n",
-    "\n",
-    "def make_grpo_config(**kwargs):\n",
-    "    supported = inspect.signature(GRPOConfig).parameters\n",
-    "    filtered = {key: value for key, value in kwargs.items() if key in supported}\n",
-    "    ignored = sorted(set(kwargs) - set(filtered))\n",
-    "    if ignored:\n",
-    "        print(\"Ignoring unsupported GRPOConfig args for this TRL version:\", ignored)\n",
-    "    return GRPOConfig(**filtered)\n",
-    "\n",
-    "training_args = make_grpo_config(\n",
-    "    output_dir=OUTPUT_DIR,\n",
-    "    max_steps=MAX_STEPS,\n",
-    "    learning_rate=5e-6,\n",
-    "    per_device_train_batch_size=2,\n",
-    "    gradient_accumulation_steps=2,\n",
-    "    num_generations=NUM_GENERATIONS,\n",
-    "    max_prompt_length=1024,\n",
-    "    max_completion_length=384,\n",
-    "    logging_steps=1,\n",
-    "    save_steps=MAX_STEPS,\n",
-    "    report_to=\"none\",\n",
-    "    bf16=bool(torch.cuda.is_available() and is_bfloat16_supported()),\n",
-    "    fp16=bool(torch.cuda.is_available() and not is_bfloat16_supported()),\n",
-    "    remove_unused_columns=False,\n",
-    ")\n",
-    "\n",
-    "trainer_kwargs = dict(\n",
-    "    model=model,\n",
-    "    reward_funcs=[openenv_reward],\n",
-    "    args=training_args,\n",
-    "    train_dataset=train_dataset,\n",
-    ")\n",
+    "# 9. Build the Dataset\n",
+    "import math\n",
+    "from datasets import Dataset\n",
+    "from collections import Counter, defaultdict\n",
+    "\n",
+    "DEFAULT_SOLVER_WEIGHT = 2\n",
+    "TARGETED_PROMPT_RATIO = 0.75\n",
+    "\n",
+    "def choose_proposer_bug_focus(seed_id: str, operators: list, operator_counts: Counter, focus_counters: Counter, row_index: int, total_rows: int) -> str | None:\n",
+    "    unique_operators = sorted(set(operators), key=lambda op: (operator_counts[op], op))\n",
+    "    if not unique_operators: return None\n",
+    "    if row_index >= math.ceil(total_rows * TARGETED_PROMPT_RATIO): return None\n",
+    "    chosen = min(unique_operators, key=lambda op: (focus_counters[op], operator_counts[op], op))\n",
+    "    focus_counters[chosen] += 1\n",
+    "    return chosen\n",
+    "\n",
+    "def build_weighted_proposer_rows(bug_bank, target_proposer_rows: int) -> list:\n",
+    "    if target_proposer_rows <= 0: return []\n",
+    "    operator_counts = Counter(sample.bug_operator for sample in bug_bank.train_samples)\n",
+    "    seed_to_operators = defaultdict(list)\n",
+    "    for sample in bug_bank.train_samples:\n",
+    "        seed_to_operators[sample.seed_id].append(sample.bug_operator)\n",
+    "        \n",
+    "    seed_weights = {seed.seed_id: 2 for seed in SEED_BANK} # Default weight for inline\n",
+    "    rows = []\n",
+    "    focus_counters = Counter()\n",
+    "    ordered_seeds = sorted(SEED_BANK, key=lambda seed: (-seed_weights[seed.seed_id], seed.seed_id))\n",
+    "\n",
+    "    for seed in SEED_BANK[:target_proposer_rows]:\n",
+    "        bug_focus = choose_proposer_bug_focus(seed.seed_id, seed_to_operators[seed.seed_id], operator_counts, focus_counters, len(rows), target_proposer_rows)\n",
+    "        prompt_text = sample_proposer_prompt(seed.original_code, bug_focus=bug_focus)\n",
+    "        rows.append({\"prompt\": [{\"role\": \"user\", \"content\": prompt_text}], \"role\": \"proposer\", \"seed_id\": seed.seed_id, \"original_code\": seed.original_code, \"bug_focus\": bug_focus if bug_focus else \"\"})\n",
+    "\n",
+    "    while len(rows) < target_proposer_rows:\n",
+    "        for seed in ordered_seeds:\n",
+    "            extra_weight = max(0, seed_weights[seed.seed_id] - 1)\n",
+    "            for _ in range(extra_weight):\n",
+    "                if len(rows) >= target_proposer_rows: break\n",
+    "                bug_focus = choose_proposer_bug_focus(seed.seed_id, seed_to_operators[seed.seed_id], operator_counts, focus_counters, len(rows), target_proposer_rows)\n",
+    "                prompt_text = sample_proposer_prompt(seed.original_code, bug_focus=bug_focus)\n",
+    "                rows.append({\"prompt\": [{\"role\": \"user\", \"content\": prompt_text}], \"role\": \"proposer\", \"seed_id\": seed.seed_id, \"original_code\": seed.original_code, \"bug_focus\": bug_focus if bug_focus else \"\"})\n",
+    "            if len(rows) >= target_proposer_rows: break\n",
+    "    return rows\n",
+    "\n",
+    "def build_mixed_role_dataset(bug_bank) -> Dataset:\n",
+    "    rows = []\n",
+    "    for bug_sample in bug_bank.train_samples:\n",
+    "        prompt_text = sample_solver_prompt(bug_sample.buggy_code, bug_sample.execution_result)\n",
+    "        rows.append({\n",
+    "            \"prompt\": [{\"role\": \"user\", \"content\": prompt_text}],\n",
+    "            \"role\": \"solver\", \"seed_id\": bug_sample.seed_id, \"original_code\": bug_sample.original_code, \"buggy_code\": bug_sample.buggy_code\n",
+    "        })\n",
+    "    target_proposer_rows = max(1, math.ceil(len(rows) / DEFAULT_SOLVER_WEIGHT)) if rows else len(SEED_BANK)\n",
+    "    rows.extend(build_weighted_proposer_rows(bug_bank, target_proposer_rows))\n",
+    "    return Dataset.from_list(rows)\n",
     "\n",
-    "try:\n",
-    "    trainer = GRPOTrainer(processing_class=tokenizer, **trainer_kwargs)\n",
-    "except TypeError:\n",
-    "    trainer = GRPOTrainer(tokenizer=tokenizer, **trainer_kwargs)\n",
-    "\n",
-    "if RUN_TRAINING:\n",
-    "    train_result = trainer.train()\n",
-    "    trainer.save_model(OUTPUT_DIR)\n",
-    "else:\n",
-    "    train_result = None\n",
-    "    print(\"RUN_TRAINING=False, trainer configured but not executed.\")"
+    "dataset, bug_bank = build_mixed_role_dataset(build_bug_bank()), build_bug_bank()\n",
+    "print(\"Dataset size:\", len(dataset))"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "after-eval",
+   "id": "e85498cb",
    "metadata": {},
    "outputs": [],
    "source": [
-    "trained_rewards, trained_completions = evaluate_policy(train_dataset, n=EVAL_SAMPLES)\n",
-    "print(\"Baseline rewards:\", baseline_rewards)\n",
-    "if baseline_rewards:\n",
-    "    print(\"Baseline mean:\", sum(baseline_rewards) / len(baseline_rewards))\n",
-    "print(\"Trained rewards:\", trained_rewards)\n",
-    "if trained_rewards:\n",
-    "    print(\"Trained mean:\", sum(trained_rewards) / len(trained_rewards))"
+    "# 10. TRL GRPO Training Setup\n",
+    "import torch\n",
+    "import importlib.util\n",
+    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+    "from trl import GRPOConfig, GRPOTrainer\n",
+    "import re\n",
+    "\n",
+    "DEFAULT_MODEL_ID = \"Qwen/Qwen2.5-Coder-3B-Instruct\" # Recommended default from DebugZero\n",
+    "DEFAULT_MAX_PROMPT_LENGTH = 768\n",
+    "DEFAULT_MAX_COMPLETION_LENGTH = 256\n",
+    "\n",
+    "def extract_python_code(text: str) -> str:\n",
+    "    match = re.search(r\"```(?:python)?\\s(.*?)```\", text, flags=re.DOTALL)\n",
+    "    if match: return match.group(1).strip()\n",
+    "    return text.strip()\n",
+    "\n",
+    "def completion_to_text(completion) -> str:\n",
+    "    if isinstance(completion, list) and completion:\n",
+    "        item = completion[0]\n",
+    "        return item.get(\"content\", \"\") if isinstance(item, dict) else str(item)\n",
+    "    return str(completion)\n",
+    "\n",
+    "def execute_candidate(seed: SeedSpec, candidate_code: str) -> dict[str, object]:\n",
+    "    result = execute_code(candidate_code, seed.test)\n",
+    "    execution_result = result.output[:500] if result.output else \"\"\n",
+    "    return {\n",
+    "        \"tests_passed\": result.passed, \"syntax_error\": result.syntax_error,\n",
+    "        \"unsafe_code\": execution_result.startswith(\"Unsafe import detected.\"),\n",
+    "        \"execution_result\": execution_result,\n",
+    "    }\n",
+    "\n",
+    "def prop_rew(prompts, completions, **kwargs):\n",
+    "    rewards = []\n",
+    "    roles = kwargs.get(\"role\", [])\n",
+    "    seed_ids = kwargs.get(\"seed_id\", [])\n",
+    "    original_codes = kwargs.get(\"original_code\", [])\n",
+    "    for i, completion in enumerate(completions):\n",
+    "        role = roles[i] if i < len(roles) else roles[0]\n",
+    "        if role != \"proposer\":\n",
+    "            rewards.append(0.0)\n",
+    "            continue\n",
+    "            \n",
+    "        seed_id = seed_ids[i] if i < len(seed_ids) else seed_ids[0]\n",
+    "        original_code = original_codes[i] if i < len(original_codes) else original_codes[0]\n",
+    "        seed = get_seed_by_id(seed_id)\n",
+    "        candidate_code = extract_python_code(completion_to_text(completion))\n",
+    "        exec_meta = execute_candidate(seed, candidate_code)\n",
+    "        \n",
+    "        unchanged = is_effectively_unchanged(original_code, candidate_code)\n",
+    "        proposer_meta = {\n",
+    "            \"seed_id\": seed.seed_id, \"tests_passed\": exec_meta[\"tests_passed\"], \"syntax_error\": exec_meta[\"syntax_error\"],\n",
+    "            \"unsafe_code\": exec_meta[\"unsafe_code\"], \"unchanged_code\": unchanged,\n",
+    "            \"changed_but_passing\": (not unchanged) and exec_meta[\"tests_passed\"] and (not exec_meta[\"syntax_error\"]),\n",
+    "            \"plausibility_score\": 0.0 if exec_meta[\"syntax_error\"] else compute_ast_distance(original_code, candidate_code)\n",
+    "        }\n",
+    "        rewards.append(compute_proposer_reward(proposer_meta))\n",
+    "    return rewards\n",
+    "\n",
+    "def solv_rew(prompts, completions, **kwargs):\n",
+    "    rewards = []\n",
+    "    roles = kwargs.get(\"role\", [])\n",
+    "    seed_ids = kwargs.get(\"seed_id\", [])\n",
+    "    for i, completion in enumerate(completions):\n",
+    "        role = roles[i] if i < len(roles) else roles[0]\n",
+    "        if role != \"solver\":\n",
+    "            rewards.append(0.0)\n",
+    "            continue\n",
+    "            \n",
+    "        seed_id = seed_ids[i] if i < len(seed_ids) else seed_ids[0]\n",
+    "        seed = get_seed_by_id(seed_id)\n",
+    "        candidate_code = extract_python_code(completion_to_text(completion))\n",
+    "        exec_meta = execute_candidate(seed, candidate_code)\n",
+    "        \n",
+    "        rewards.append(compute_solver_reward({\"seed_id\": seed.seed_id, \"tests_passed\": exec_meta[\"tests_passed\"], \"syntax_error\": exec_meta[\"syntax_error\"], \"unsafe_code\": exec_meta[\"unsafe_code\"]}))\n",
+    "    return rewards\n",
+    "\n",
+    "# Load Model\n",
+    "model, tokenizer = None, None\n",
+    "try:\n",
+    "    from unsloth import FastLanguageModel, PatchFastRL\n",
+    "    PatchFastRL(\"GRPO\", FastLanguageModel)\n",
+    "    model, tokenizer = FastLanguageModel.from_pretrained(model_name=\"unsloth/Qwen2.5-Coder-3B-Instruct\", max_seq_length=1024, load_in_4bit=True, fast_inference=True)\n",
+    "    model = FastLanguageModel.get_peft_model(model, r=16, target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"], lora_alpha=16, bias=\"none\", use_gradient_checkpointing=\"unsloth\")\n",
+    "except ImportError:\n",
+    "    # Unsloth is failing to load (e.g., due to Kaggle/Colab CUDA mismatch).\n",
+    "    # Falling back to standard HuggingFace PEFT (LoRA).\n",
+    "    from peft import LoraConfig, get_peft_model\n",
+    "    tokenizer = AutoTokenizer.from_pretrained(DEFAULT_MODEL_ID)\n",
+    "    model = AutoModelForCausalLM.from_pretrained(DEFAULT_MODEL_ID, torch_dtype=torch.bfloat16, device_map=\"auto\")\n",
+    "    peft_config = LoraConfig(r=16, lora_alpha=16, target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"], lora_dropout=0, bias=\"none\", task_type=\"CAUSAL_LM\")\n",
+    "    model = get_peft_model(model, peft_config)\n",
+    "\n",
+    "if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "plots",
+   "id": "c9cac092",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import os\n",
+    "# 11. Run GRPO Training & Plot Metrics\n",
+    "has_bitsandbytes = importlib.util.find_spec(\"bitsandbytes\") is not None\n",
+    "\n",
+    "training_args = GRPOConfig(\n",
+    "    output_dir=\"debugzero_model\",\n",
+    "    per_device_train_batch_size=1,\n",
+    "    gradient_accumulation_steps=4,\n",
+    "    learning_rate=2e-5,\n",
+    "    max_steps=50,\n",
+    "    num_generations=2,\n",
+    "    max_prompt_length=DEFAULT_MAX_PROMPT_LENGTH,\n",
+    "    max_completion_length=DEFAULT_MAX_COMPLETION_LENGTH,\n",
+    "    bf16=False, fp16=True,\n",
+    "    logging_steps=5,\n",
+    "    optim=\"adamw_8bit\" if has_bitsandbytes else \"adamw_torch\",\n",
+    "    report_to=\"none\",\n",
+    "    disable_tqdm=True,\n",
+    ")\n",
+    "\n",
+    "from transformers import TrainerCallback\n",
+    "\n",
+    "class TableMetricsCallback(TrainerCallback):\n",
+    "    def on_train_begin(self, args, state, control, **kwargs):\n",
+    "        print(f\"{'Step':<8} | {'Loss':<10} | {'Prop Rew':<10} | {'Solv Rew':<10} | {'Tot Rew':<10} | {'Entropy':<10}\")\n",
+    "        print(\"-\" * 70)\n",
+    "        \n",
+    "    def on_log(self, args, state, control, logs=None, **kwargs):\n",
+    "        if logs and \"loss\" in logs:\n",
+    "            loss = logs.get(\"loss\", 0.0)\n",
+    "            p_reward = logs.get(\"rewards/prop_rew/mean\", 0.0)\n",
+    "            s_reward = logs.get(\"rewards/solv_rew/mean\", 0.0)\n",
+    "            total_reward = logs.get(\"reward\", p_reward + s_reward)\n",
+    "            entropy = logs.get(\"entropy\", 0.0)\n",
+    "            print(f\"{state.global_step:<8} | {loss:<10.4f} | {p_reward:<10.4f} | {s_reward:<10.4f} | {total_reward:<10.4f} | {entropy:<10.4f}\")\n",
+    "\n",
+    "trainer = GRPOTrainer(\n",
+    "\n",
+    "    \n",
+    "\n",
+    "    model=model,\n",
+    "    reward_funcs=[prop_rew, solv_rew],\n",
+    "    args=training_args,\n",
+    "    train_dataset=dataset,\n",
+    "    processing_class=tokenizer,\n",
+    "    callbacks=[TableMetricsCallback()],\n",
+    ")\n",
+    "\n",
+    "print(f\"Starting GRPO training for {training_args.max_steps} episodes (steps)...\")\n",
+    "print(\"To change the number of episodes, modify 'max_steps' in GRPOConfig above.\")\n",
+    "train_result = trainer.train()\n",
+    "print(\"Training Complete! View debugzero_model for artifacts.\")\n",
     "\n",
+    "# 12. Plot Metrics natively in Colab\n",
     "import matplotlib.pyplot as plt\n",
-    "import pandas as pd\n",
-    "\n",
-    "os.makedirs(\"results\", exist_ok=True)\n",
-    "history = pd.DataFrame(getattr(trainer.state, \"log_history\", []))\n",
-    "history.to_csv(\"results/training_log.csv\", index=False)\n",
-    "\n",
-    "reward_cols = [col for col in history.columns if \"reward\" in col.lower()]\n",
-    "loss_cols = [col for col in history.columns if \"loss\" in col.lower()]\n",
-    "\n",
-    "if \"step\" in history.columns and reward_cols:\n",
-    "    ax = history.plot(x=\"step\", y=reward_cols, marker=\"o\", figsize=(8, 4))\n",
-    "    ax.set_xlabel(\"training step\")\n",
-    "    ax.set_ylabel(\"reward\")\n",
-    "    ax.set_title(\"DebugZero OpenEnv reward during GRPO\")\n",
-    "    plt.tight_layout()\n",
-    "    plt.savefig(\"results/reward_curve.png\", dpi=160)\n",
-    "    plt.show()\n",
-    "else:\n",
-    "    print(\"No reward columns found in trainer history. Columns:\", list(history.columns))\n",
-    "\n",
-    "if \"step\" in history.columns and loss_cols:\n",
-    "    ax = history.plot(x=\"step\", y=loss_cols, marker=\"o\", figsize=(8, 4))\n",
-    "    ax.set_xlabel(\"training step\")\n",
-    "    ax.set_ylabel(\"loss\")\n",
-    "    ax.set_title(\"DebugZero GRPO loss\")\n",
-    "    plt.tight_layout()\n",
-    "    plt.savefig(\"results/loss_curve.png\", dpi=160)\n",
-    "    plt.show()\n",
-    "else:\n",
-    "    print(\"No loss columns found in trainer history. Columns:\", list(history.columns))\n",
-    "\n",
-    "comparison = pd.DataFrame({\n",
-    "    \"phase\": [\"baseline\", \"trained\"],\n",
-    "    \"mean_reward\": [\n",
-    "        sum(baseline_rewards) / len(baseline_rewards) if baseline_rewards else 0.0,\n",
-    "        sum(trained_rewards) / len(trained_rewards) if trained_rewards else 0.0,\n",
-    "    ],\n",
-    "})\n",
-    "ax = comparison.plot.bar(x=\"phase\", y=\"mean_reward\", legend=False, figsize=(5, 4))\n",
-    "ax.set_xlabel(\"policy\")\n",
-    "ax.set_ylabel(\"mean live OpenEnv reward\")\n",
-    "ax.set_title(\"Before vs after training\")\n",
+    "\n",
+    "log_history = trainer.state.log_history\n",
+    "steps = [entry[\"step\"] for entry in log_history if \"loss\" in entry]\n",
+    "losses = [entry[\"loss\"] for entry in log_history if \"loss\" in entry]\n",
+    "\n",
+    "p_rewards = []\n",
+    "s_rewards = []\n",
+    "\n",
+    "for entry in log_history:\n",
+    "    if \"loss\" in entry:\n",
+    "        p_rewards.append(entry.get(\"rewards/prop_rew/mean\", entry.get(\"rewards/prop_rew/mean\", 0.0)))\n",
+    "        s_rewards.append(entry.get(\"rewards/solv_rew/mean\", entry.get(\"rewards/solv_rew/mean\", 0.0)))\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
+    "\n",
+    "# Loss Plot\n",
+    "if steps and losses:\n",
+    "    axes[0].plot(steps[:len(losses)], losses, marker='o', color='purple', label=\"Total Loss\")\n",
+    "    axes[0].set_title(\"GRPO Training Loss\")\n",
+    "    axes[0].set_xlabel(\"Steps (Episodes)\")\n",
+    "    axes[0].set_ylabel(\"Loss\")\n",
+    "    axes[0].grid(True, linestyle=\"--\", alpha=0.5)\n",
+    "    axes[0].legend()\n",
+    "\n",
+    "# Rewards Plot\n",
+    "if steps and (p_rewards or s_rewards):\n",
+    "    if p_rewards:\n",
+    "        axes[1].plot(steps[:len(p_rewards)], p_rewards, marker='s', color='orange', label=\"Proposer Reward\")\n",
+    "    if s_rewards:\n",
+    "        axes[1].plot(steps[:len(s_rewards)], s_rewards, marker='^', color='green', label=\"Solver Reward\")\n",
+    "        \n",
+    "    axes[1].set_title(\"GRPO Environment Rewards Evolution\")\n",
+    "    axes[1].set_xlabel(\"Steps (Episodes)\")\n",
+    "    axes[1].set_ylabel(\"Reward\")\n",
+    "    axes[1].grid(True, linestyle=\"--\", alpha=0.5)\n",
+    "    axes[1].legend()\n",
+    "\n",
     "plt.tight_layout()\n",
-    "plt.savefig(\"results/baseline_vs_trained_reward.png\", dpi=160)\n",
-    "plt.show()\n",
-    "comparison"
+    "plt.show()"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "readme-note",
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0048cecb",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## Submission Note\n",
-    "\n",
-    "After running the notebook, attach or commit the generated files under `results/` and link this notebook from the README. Judges should be able to see the GitHub install, OpenEnv smoke test, reward sanity checks, baseline-vs-trained comparison, and the plots."
+    "# 13. Interactive Verification\n",
+    "# We wrap tqdm around some final manual checks to give a visual indicator for eval.\n",
+    "from tqdm.auto import tqdm\n",
+    "\n",
+    "print(\"Running final evaluations across the holdout set:\")\n",
+    "model.eval()\n",
+    "\n",
+    "# Testing Solver\n",
+    "correct = 0\n",
+    "total_evals = len(bug_bank.eval_samples)\n",
+    "\n",
+    "print(f\"Validating {total_evals} Holdout bugs...\")\n",
+    "for sample in tqdm(bug_bank.eval_samples, desc=\"Solver Eval\"):\n",
+    "    prompt = sample_solver_prompt(sample.buggy_code, sample.execution_result)\n",
+    "    \n",
+    "    prompt_text = tokenizer.apply_chat_template([{\"role\": \"user\", \"content\": prompt}], tokenize=False, add_generation_prompt=True)\n",
+    "    encoded = tokenizer(prompt_text, return_tensors=\"pt\").to(model.device)\n",
+    "    out = model.generate(**encoded, max_new_tokens=200, pad_token_id=tokenizer.pad_token_id, do_sample=False)\n",
+    "    \n",
+    "    generated_code = tokenizer.decode(out[0][encoded.input_ids.shape[-1]:], skip_special_tokens=True)\n",
+    "    clean_code = extract_python_code(generated_code)\n",
+    "    \n",
+    "    # Check if the generated solution passes the test\n",
+    "    seed = get_seed_by_id(sample.seed_id)\n",
+    "    exec_meta = execute_candidate(seed, clean_code)\n",
+    "    \n",
+    "    if exec_meta[\"tests_passed\"] and not exec_meta[\"syntax_error\"]:\n",
+    "        correct += 1\n",
+    "\n",
+    "print(f\"Holdout Set Solver Pass Rate: {correct}/{total_evals} ({correct/total_evals:.1%})\")"
    ]
   }
  ],
  "metadata": {
-  "accelerator": "GPU",
-  "colab": {
-   "gpuType": "T4"
-  },
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -610,7 +827,7 @@
   },
   "language_info": {
    "name": "python",
-   "pygments_lexer": "ipython3"
+   "version": "3.11"
   }
  },
  "nbformat": 4,

openenv.yaml

@@ -1,9 +1,9 @@
-spec_version: 1
-name: debugZero
-description: "Our environment extends the Absolute Zero paradigm (Zhao et al., NeurIPS 2025) to adversarial bug-fixing self-play. The Proposer's mutation operators are implemented from scratch using Python AST manipulation across 8 operator types. The verifier adapts Mutahunter's execution pipeline."
-type: space
-runtime: fastapi
-app: server.app:app
-port: 8000
-workers: 4
-max_concurrent_envs: 100
+spec_version: 1
+name: debugZero
+description: "Our environment DebugZero is adversarial bug-fixing self-play. The Proposer's mutation operators are implemented from scratch using Python AST manipulation across 8 operator types. The verifier adapts Mutahunter's execution pipeline."
+type: space
+runtime: fastapi
+app: server.app:app
+port: 8000
+workers: 4
+max_concurrent_envs: 100

openenv_debugZero.egg-info/PKG-INFO

@@ -5,8 +5,10 @@ Summary: Debugzero environment for OpenEnv
 Requires-Python: >=3.10
 Requires-Dist: datasets>=4.8.4
 Requires-Dist: openenv-core[core]>=0.2.1
+Requires-Dist: openai>=1.0.0
 Requires-Dist: pytest>=9.0.3
 Requires-Dist: thefuzz>=0.22.1
+Requires-Dist: matplotlib>=3.8.0
 Requires-Dist: transformers>=5.6.2
 Requires-Dist: trl>=1.2.0
 Provides-Extra: dev

openenv_debugZero.egg-info/SOURCES.txt

@@ -1,21 +1,24 @@
-README.md
-__init__.py
-client.py
-models.py
-pyproject.toml
-./__init__.py
-./client.py
-./models.py
-./run_episode.py
-openenv_debugZero.egg-info/PKG-INFO
-openenv_debugZero.egg-info/SOURCES.txt
-openenv_debugZero.egg-info/dependency_links.txt
-openenv_debugZero.egg-info/entry_points.txt
-openenv_debugZero.egg-info/requires.txt
-openenv_debugZero.egg-info/top_level.txt
-server/__init__.py
-server/app.py
-server/bug_injector.py
-server/debugZero_environment.py
-server/executor.py
-server/plausibility.py
+README.md
+__init__.py
+bug_bank.py
+client.py
+models.py
+pyproject.toml
+seed_bank.py
+./__init__.py
+./bug_bank.py
+./client.py
+./models.py
+./seed_bank.py
+openenv_debugZero.egg-info/PKG-INFO
+openenv_debugZero.egg-info/SOURCES.txt
+openenv_debugZero.egg-info/dependency_links.txt
+openenv_debugZero.egg-info/entry_points.txt
+openenv_debugZero.egg-info/requires.txt
+openenv_debugZero.egg-info/top_level.txt
+server/__init__.py
+server/app.py
+server/bug_injector.py
+server/debugZero_environment.py
+server/executor.py
+server/rewards.py