| # PromptOps Arena — Scope (Locked) | |
| > Locked at T+0. Any feature not on this page is OUT OF SCOPE for the 48h hackathon. | |
| ## Thesis (one sentence) | |
| An OpenEnv RL environment where an agent learns, via GRPO, to write and iteratively edit prompts that maximize verifiable task success on a frozen LLM-under-test, across math/code/JSON tasks — demonstrating *transferable* prompt-engineering strategy as a learned skill. | |
| ## Models (locked) | |
| | Role | Model | Notes | | |
| |---|---|---| | |
| | Agent (trained) | `Qwen/Qwen2.5-1.5B-Instruct` + LoRA | Trained with GRPO via Unsloth, 4-bit | | |
| | LLM-under-test (frozen) | `Qwen/Qwen2.5-0.5B-Instruct` | Never trained. Loaded once at module top. | | |
| ## Tasks (locked) | |
| | Type | Source | Count (train) | Count (test, held-out) | Verifier | | |
| |---|---|---|---|---| | |
| | Math | GSM8K subset | 30 | 10 | Exact match on `\boxed{}` or `<answer>` extraction | | |
| | Code | MBPP subset | 20 | 10 | Subprocess `exec` with timeout, run unit tests | | |
| | JSON extraction | Hand-built | 10 | 10 | `jsonschema.validate` on parsed output | | |
| Total: 60 train / 30 test. | |
| ## Episode contract | |
| - Agent receives task text + task type + previous prompt (if any) + previous completion (if any) + previous reward | |
| - Agent emits a **new full system prompt** (replace, not diff — simplest action space) | |
| - Env runs LLM-under-test with `[system_prompt, user_task]` once | |
| - Verifier returns 0/1 correctness + format/brevity bonuses | |
| - Episode terminates when correctness == 1.0 OR `edit_turn >= 3` | |
| ## Reward (locked) | |
| ``` | |
| total = correctness + 0.1 * format_bonus + brevity_penalty | |
| ``` | |
| - `correctness ∈ {0, 1}` — programmatic verifier | |
| - `format_bonus ∈ {0, 1}` — required tags present | |
| - `brevity_penalty ∈ [-0.1, 0]` — only if prompt > 800 chars | |
| - All components logged separately | |
| ## Compute budget | |
| - Local smoke tests: CPU + small batches, Windows | |
| - Full training: **HF Jobs `a10g-large`, ≤2h timeout** | |
| - Demo: **HuggingFace Space, ZeroGPU** | |
| ## Out of scope (will not build) | |
| - Multi-agent / hierarchical agents | |
| - RAG, web search, tool use beyond verifier | |
| - Persistent memory across episodes | |
| - Custom reward model (we use programmatic verifiers) | |
| - vLLM serving (transformers `generate()` is fine for 0.5B) | |
| - Public Docker Space for env (in-process env in Gradio Space is enough) | |
| - 4th task type (translation/summarization) | |
| - 3B agent (only if Phase 5 has >2h slack) | |
| ## Submission targets | |
| - HuggingFace Space: `<user>/promptops-arena` | |
| - Model repo: `<user>/promptops-arena-agent` (LoRA adapter only) | |
| - GitHub repo: public | |
| - Video: ≤90 seconds, hosted on YouTube/Loom (UNLISTED), linked from README — never committed | |
| ## Judging weights | |
| - Environment Innovation 40% | |
| - Storytelling & Presentation 30% | |
| - Showing Improvement in Rewards 20% | |
| - Reward & Training Pipeline 10% | |
| ## Time gates (hard) | |
| - T+24h: training must have started or drop to 2 task types | |
| - T+36h: must have reward improvement; else ship "untrained agent vs zero-shot" | |
| - T+44h: README + video + Space MUST be done; freeze features | |