| ---
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| title: ForgeEnv
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| emoji: 🔧
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| colorFrom: indigo
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| colorTo: green
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| sdk: docker
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| app_port: 7860
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| pinned: true
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| license: apache-2.0
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| tags:
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| - openenv
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| - self-play
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| - self-improvement
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| - code-repair
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| - schema-drift
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| - reinforcement-learning
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| - huggingface
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| short_description: Self-improving RL env for HF library-drift repair
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| ---
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|
|
| # ForgeEnv — OpenEnv Server
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|
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| This Space hosts the **ForgeEnv** OpenEnv-compliant environment as a FastAPI
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| service. It exposes the standard `reset`, `step`, and `state` endpoints and is
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| the runtime that training notebooks (TRL + Unsloth) connect to.
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|
|
| > **Theme:** Self-Improvement (Hackathon Theme #4) — Challenger / Solver
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| > co-evolution via R-Zero, SPIRAL, and Absolute Zero Reasoner techniques.
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|
|
| ## What it does
|
|
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| ForgeEnv simulates **HuggingFace library version drift**. A *Drift Generator*
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| proposes a realistic breakage to a working training script (renamed APIs,
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| deprecated imports, changed argument signatures, etc.). A *Repair Agent* then
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| emits a unified diff that should restore the script. Reward is computed by an
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| execution simulator + AST checker + held-out evaluator (multi-component to
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| resist reward hacking).
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|
|
| ## API
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|
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| The server uses [`openenv-core`](https://pypi.org/project/openenv-core/) and
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| follows the Gym-style contract:
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|
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| | Endpoint | Method | Purpose |
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| | -------- | ------ | -------------------------------------------------- |
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| | `/reset` | POST | Sample a fresh task, return drift-gen observation |
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| | `/step` | POST | Apply a `ForgeAction` (breakage or repair) |
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| | `/state` | GET | Inspect the current internal state |
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| | `/health`| GET | Health probe (used by the container HEALTHCHECK) |
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|
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| `ForgeAction` is a discriminated union of `BreakageAction` (used in phase 1)
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| and `RepairAction` (used in phase 2). See
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| [`forgeenv/env/actions.py`](forgeenv/env/actions.py).
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|
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| ## Quick test
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|
|
| ```bash
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| curl -X POST https://akhiilll-forgeenv.hf.space/reset
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| curl https://akhiilll-forgeenv.hf.space/state
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| ```
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|
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| ```python
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| from openenv.core.env_client import EnvClient
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|
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| async with EnvClient(base_url="https://akhiilll-forgeenv.hf.space") as client:
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| obs = await client.reset()
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| print(obs.observation.current_phase, obs.observation.task_id)
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| ```
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|
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| ## Project links
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|
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| - **Main repo / training notebooks / plots:**
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| <https://github.com/akhiilll/forgeenv>
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| - **Repair Agent model (LoRA):**
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| <https://huggingface.co/akhiilll/forgeenv-repair-agent>
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| - **Demo (Gradio + ZeroGPU):**
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| <https://huggingface.co/spaces/akhiilll/forgeenv-demo>
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|
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| ## Citations
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|
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| - Huang et al., *R-Zero: Self-Evolving Reasoning LLM From Zero Data* (2025)
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| - Zhao et al., *Absolute Zero: Reinforced Self-play Reasoning with Zero Data* (2025)
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| - Liu et al., *SPIRAL: Self-Play on Zero-Sum Games* (2025)
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| - [arXiv:2408.10215](https://arxiv.org/abs/2408.10215) — Reward engineering & shaping
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| - [arXiv:2601.19100](https://arxiv.org/abs/2601.19100) — Reward engineering for RL in software tasks
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|
|
