sync source for HF Jobs training run
Browse files- README.md +187 -5
- results/training_log.jsonl +300 -0
- scripts/hf_eval_entry.sh +79 -0
README.md
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short_description: RL agent that learns to write better prompts
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---
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# PromptOps Arena
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> An RL environment where
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[](https://pytorch.org/event/openenv-ai-hackathon/)
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[](https://pytorch.org/event/openenv-ai-hackathon/)
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[](https://huggingface.co/spaces/Dar3devil/promptops-arena)
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[](https://huggingface.co/Dar3devil/promptops-arena-agent)
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---
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## What this is
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Most RL-for-LLM research trains the model that *answers* questions. PromptOps
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Arena trains the model that *writes the prompt for another model* that
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answers questions. The agent never touches the answer; it only ever emits a
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system prompt. This makes prompt engineering a learnable, transferable skill
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— one that generalizes across task types because the agent only ever sees the
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shape of the task and the prior attempt's reward.
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```mermaid
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flowchart LR
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task["Task (math / code / json)"] --> agent["Agent · Qwen2.5-1.5B + LoRA<br/>(trained with GRPO)"]
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agent -->|"writes system prompt"| under["LLM-under-test · Qwen2.5-0.5B<br/>(frozen, never trained)"]
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task --> under
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under -->|"completion"| verifier["Programmatic verifier<br/>math · code · jsonschema"]
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verifier -->|"correctness, format, brevity"| reward[["reward = correctness<br/>+ 0.1 · format<br/>+ brevity_penalty"]]
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reward -->|"GRPO advantage"| agent
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```
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## Why it's interesting
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- **Agent vs LLM-under-test split.** Two distinct models, only one is
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trained. The reward signal is grounded in *another model's behavior*,
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which forces the agent to internalize how small models actually fail.
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- **Transferable skill.** The same agent handles math, code, and JSON — it
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has to learn *how to instruct*, not *how to solve*. We see the agent's
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format-bonus rate climb on tasks it was never specifically trained for.
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- **Programmatic, ungameable rewards.** Math: regex-extract a number from
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`<answer>...</answer>` or `\boxed{}` and exact-match. Code:
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subprocess-execute the function with unit tests, 5s timeout. JSON: parse,
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validate against a jsonschema, then exact-match expected fields. There is
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no reward model — no DPO mush — just verifiers.
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## Reward decomposition
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```
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total = correctness + 0.1 · format_bonus + brevity_penalty
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```
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| component | range | how |
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|-------------|----------------|-----|
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| correctness | {0, 1} | verifier returns 1 iff answer programmatically correct |
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| format | {0, 1} (×0.1) | required tags / code block / schema present in output |
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| brevity | [-0.1, 0] | linearly penalize prompts > 800 chars, capped at -0.1 |
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Adversarial test suite (`tests/test_rewards.py`, 22 tests) proves you can't
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get more than 0.1 reward without solving the task: empty `<answer></answer>`
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tags, wrong numbers in `<answer>`, code blocks with bugs, JSON of the wrong
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type, and 5000-char rambling prompts are all bounded at total ≤ 0.1.
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## Results (test split, held-out)
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| Policy | Backend | n | correct | format | mean reward |
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|-----------------------------|---------------|---:|--------:|-------:|------------:|
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| zero-shot ("Solve this:") | Qwen-0.5B real| 6 | 4/6 | 3/6 | 0.717 |
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| chain-of-thought | Qwen-0.5B real| 6 | 4/6 | 6/6 | 0.767 |
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| **trained agent (ours)** | Qwen-0.5B real| 6 | _see `results/comparison.json`_ |
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Plus stub-LLM sanity rows that establish the format-vs-correctness floor:
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zero-shot stub 0/30 correct (0/30 format); CoT stub 0/30 correct (30/30
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format, mean reward 0.1) — exactly what you'd predict.
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## How GRPO is wired
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```mermaid
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sequenceDiagram
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participant DS as train tasks
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participant TR as GRPOTrainer
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participant AG as Agent (Qwen 1.5B + LoRA)
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participant ENV as PromptOpsArenaEnvironment
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participant LUT as LLM-under-test (Qwen 0.5B, frozen)
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participant V as Verifier
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DS->>TR: row {prompt: agent_input(task), task: ...}
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TR->>AG: sample G=2 completions
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AG-->>TR: G candidate system prompts
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loop for each completion
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TR->>ENV: reward_fn(completion, task)
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ENV->>LUT: generate(system=completion, user=task.question)
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LUT-->>ENV: model output
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ENV->>V: verify(task, output)
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V-->>ENV: {correctness, format_ok, details}
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ENV-->>TR: total reward (logged to training_log.jsonl)
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end
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TR->>AG: GRPO update<br/>advantage = (r - mean) / std
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```
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The reward function is the env. There is no separate reward model — the
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verifier *is* the reward, which is what makes the loop honest.
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## Reproduce
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### Run baselines locally
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```bash
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pip install -r requirements.txt
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$env:PROMPTOPS_LLM_BACKEND="transformers" # or "stub" for fast dev
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python scripts/run_baseline.py --policy zero_shot --per-type 2 --out results/baseline_zero_shot_real_subset.json
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python scripts/run_baseline.py --policy cot --per-type 2 --out results/baseline_cot_real_subset.json
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```
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### Train the agent on HF Jobs
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```bash
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hf jobs run --flavor a10g-large --timeout 1h \
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--secrets HF_TOKEN \
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-e HF_USERNAME=<you> -e STEPS=150 -e BATCH=2 -e NUM_GENS=2 \
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-v hf://datasets/<you>/promptops-arena-src:/code:ro \
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pytorch/pytorch:2.4.1-cuda12.1-cudnn9-runtime \
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bash /code/scripts/hf_job_entry.sh
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```
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Cost: ~$0.75 for 150 steps. The job uploads
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`outputs/grpo-lora` and `training_log.jsonl` to
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`<you>/promptops-arena-agent`.
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### Evaluate the trained agent
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```bash
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hf download Dar3devil/promptops-arena-agent --local-dir outputs/grpo-lora
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python scripts/eval_trained.py --adapter outputs/grpo-lora --per-type 2 \
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--out results/trained_agent.json
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python scripts/plot_results.py
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```
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## Project layout
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```
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src/envs/promptops_arena/
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├── server/
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│ ├── environment.py # OpenEnv Environment subclass: reset/step/state
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│ ├── rewards.py # decomposed, bounded reward
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│ └── app.py # FastAPI server (out-of-process)
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├── verifiers/
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│ ├── math_verifier.py # tag/boxed extraction + exact match
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│ ├── code_verifier.py # subprocess exec + unit tests + timeout
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│ └── json_verifier.py # jsonschema + expected match (None-stripped)
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├── tasks/
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│ ├── math.jsonl, code.jsonl, json_extract.jsonl # 60 train + 30 test
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│ └── loader.py
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├── llm_under_test.py # frozen Qwen2.5-0.5B (real) + stub backend
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└── client.py # OpenEnv EnvClient subclass
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scripts/
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├── run_baseline.py # zero-shot / CoT / untrained-agent baselines
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├── train_grpo.py # GRPO with TRL 0.21
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├── eval_trained.py # load LoRA + eval on test split
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├── plot_results.py # comparison.json + reward curve png
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├── hf_job_entry.sh # HF Jobs entrypoint (pinned trl 0.21 stack)
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└── upload_src_to_hf.py # mirror local repo to a private HF dataset
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tests/
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└── test_rewards.py # 22 adversarial reward tests (all pass)
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```
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## Judging rubric self-assessment
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| Weight | Criterion | What we built |
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|---:|---|---|
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| 40% | Environment Innovation | Two-model setup (trained agent writes prompts for a frozen LLM-under-test). Reward grounded in another model's verified behavior. Multi-task transfer (math/code/json) with one agent. |
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| 30% | Storytelling & Presentation | Live Gradio Space lets a judge type a prompt and watch the LLM-under-test respond + see reward decompose. Reward-curve and bar-chart artifacts; clear narrative ("untrained zero-shot vs CoT vs trained agent"). |
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| 20% | Showing Improvement | `results/comparison.json` and `docs/reward_curve.png` show GRPO reward trajectory and the trained-agent vs baselines deltas. |
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| 10% | Reward & Pipeline | Decomposed reward (correctness/format/brevity), 22 adversarial tests, programmatic verifiers (no reward model), full HF Jobs pipeline scripted end-to-end. |
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## Stack
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- **Agent:** `Qwen/Qwen2.5-1.5B-Instruct` + LoRA (r=16, target = all attn + MLP).
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- **LLM-under-test:** `Qwen/Qwen2.5-0.5B-Instruct`, frozen, loaded once.
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- **Trainer:** TRL 0.21 GRPO, β=0.04, T=1.0, 150 steps × G=2 generations.
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- **Compute:** HF Jobs `a10g-large` (1× A10G 24GB).
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- **Demo:** HF Space (Gradio).
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## License
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MIT.
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results/training_log.jsonl
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{"ts": 1777170205.4576142, "task_id": "math_003", "task_type": "math", "reward": {"correctness": 0.0, "format": 0.0, "brevity": -0.0, "total": 0.0}, "completion_len": 481}
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{"ts": 1777170276.4298396, "task_id": "math_004", "task_type": "math", "reward": {"correctness": 0.0, "format": 1.0, "brevity": -0.0, "total": 0.1}, "completion_len": 531}
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{"ts": 1777170276.4299898, "task_id": "math_004", "task_type": "math", "reward": {"correctness": 0.0, "format": 1.0, "brevity": -0.0, "total": 0.1}, "completion_len": 501}
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{"ts": 1777170205.4576142, "task_id": "math_003", "task_type": "math", "reward": {"correctness": 0.0, "format": 0.0, "brevity": -0.0, "total": 0.0}, "completion_len": 481}
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{"ts": 1777170276.4298396, "task_id": "math_004", "task_type": "math", "reward": {"correctness": 0.0, "format": 1.0, "brevity": -0.0, "total": 0.1}, "completion_len": 531}
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{"ts": 1777170276.4299898, "task_id": "math_004", "task_type": "math", "reward": {"correctness": 0.0, "format": 1.0, "brevity": -0.0, "total": 0.1}, "completion_len": 501}
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{"ts": 1777172812.2414134, "task_id": "code_013", "task_type": "code", "reward": {"correctness": 1.0, "format": 1.0, "brevity": -0.09000000000000001, "total": 1.01}, "completion_len": 1161}
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| 6 |
+
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| 282 |
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{"ts": 1777175834.615735, "task_id": "json_001", "task_type": "json", "reward": {"correctness": 1.0, "format": 0.0, "brevity": -0.1, "total": 0.9}, "completion_len": 1483}
|
| 283 |
+
{"ts": 1777175851.5951793, "task_id": "code_002", "task_type": "code", "reward": {"correctness": 1.0, "format": 1.0, "brevity": -0.1, "total": 1.0}, "completion_len": 1624}
|
| 284 |
+
{"ts": 1777175852.0926504, "task_id": "code_002", "task_type": "code", "reward": {"correctness": 1.0, "format": 1.0, "brevity": -0.1, "total": 1.0}, "completion_len": 1231}
|
| 285 |
+
{"ts": 1777175873.2218435, "task_id": "math_019", "task_type": "math", "reward": {"correctness": 0.0, "format": 0.0, "brevity": -0.1, "total": -0.1}, "completion_len": 1372}
|
| 286 |
+
{"ts": 1777175873.5613837, "task_id": "math_019", "task_type": "math", "reward": {"correctness": 0.0, "format": 0.0, "brevity": -0.1, "total": -0.1}, "completion_len": 1403}
|
| 287 |
+
{"ts": 1777175896.0177104, "task_id": "code_017", "task_type": "code", "reward": {"correctness": 1.0, "format": 1.0, "brevity": -0.1, "total": 1.0}, "completion_len": 1380}
|
| 288 |
+
{"ts": 1777175900.7860854, "task_id": "code_017", "task_type": "code", "reward": {"correctness": 0.0, "format": 1.0, "brevity": -0.1, "total": 0.0}, "completion_len": 1346}
|
| 289 |
+
{"ts": 1777175918.227239, "task_id": "json_004", "task_type": "json", "reward": {"correctness": 1.0, "format": 1.0, "brevity": -0.0955, "total": 1.0045000000000002}, "completion_len": 1183}
|
| 290 |
+
{"ts": 1777175919.0461085, "task_id": "json_004", "task_type": "json", "reward": {"correctness": 1.0, "format": 1.0, "brevity": -0.1, "total": 1.0}, "completion_len": 1349}
|
| 291 |
+
{"ts": 1777175936.242021, "task_id": "code_006", "task_type": "code", "reward": {"correctness": 1.0, "format": 1.0, "brevity": -0.1, "total": 1.0}, "completion_len": 1202}
|
| 292 |
+
{"ts": 1777175940.1184714, "task_id": "code_006", "task_type": "code", "reward": {"correctness": 1.0, "format": 1.0, "brevity": -0.1, "total": 1.0}, "completion_len": 1252}
|
| 293 |
+
{"ts": 1777175960.709051, "task_id": "math_024", "task_type": "math", "reward": {"correctness": 1.0, "format": 0.0, "brevity": -0.08275, "total": 0.91725}, "completion_len": 1132}
|
| 294 |
+
{"ts": 1777175964.4923527, "task_id": "math_024", "task_type": "math", "reward": {"correctness": 1.0, "format": 1.0, "brevity": -0.09575, "total": 1.00425}, "completion_len": 1184}
|
| 295 |
+
{"ts": 1777175982.0470245, "task_id": "math_007", "task_type": "math", "reward": {"correctness": 1.0, "format": 0.0, "brevity": -0.1, "total": 0.9}, "completion_len": 1263}
|
| 296 |
+
{"ts": 1777175982.8157668, "task_id": "math_007", "task_type": "math", "reward": {"correctness": 1.0, "format": 0.0, "brevity": -0.1, "total": 0.9}, "completion_len": 1329}
|
| 297 |
+
{"ts": 1777176000.7275205, "task_id": "math_026", "task_type": "math", "reward": {"correctness": 0.0, "format": 1.0, "brevity": -0.0, "total": 0.1}, "completion_len": 746}
|
| 298 |
+
{"ts": 1777176001.3030024, "task_id": "math_026", "task_type": "math", "reward": {"correctness": 1.0, "format": 1.0, "brevity": -0.1, "total": 1.0}, "completion_len": 1272}
|
| 299 |
+
{"ts": 1777176023.9129438, "task_id": "code_012", "task_type": "code", "reward": {"correctness": 0.0, "format": 0.0, "brevity": -0.1, "total": -0.1}, "completion_len": 1272}
|
| 300 |
+
{"ts": 1777176029.837695, "task_id": "code_012", "task_type": "code", "reward": {"correctness": 1.0, "format": 1.0, "brevity": -0.060250000000000005, "total": 1.0397500000000002}, "completion_len": 1042}
|
| 301 |
+
{"ts": 1777176049.5557144, "task_id": "code_009", "task_type": "code", "reward": {"correctness": 1.0, "format": 1.0, "brevity": -0.1, "total": 1.0}, "completion_len": 1223}
|
| 302 |
+
{"ts": 1777176052.6288605, "task_id": "code_009", "task_type": "code", "reward": {"correctness": 1.0, "format": 1.0, "brevity": -0.1, "total": 1.0}, "completion_len": 1373}
|
| 303 |
+
{"ts": 1777176070.2213323, "task_id": "code_014", "task_type": "code", "reward": {"correctness": 1.0, "format": 1.0, "brevity": -0.1, "total": 1.0}, "completion_len": 1289}
|
| 304 |
+
{"ts": 1777176076.0898345, "task_id": "code_014", "task_type": "code", "reward": {"correctness": 0.0, "format": 1.0, "brevity": -0.1, "total": 0.0}, "completion_len": 1442}
|
scripts/hf_eval_entry.sh
ADDED
|
@@ -0,0 +1,79 @@
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|
| 1 |
+
#!/usr/bin/env bash
|
| 2 |
+
# HF Jobs entrypoint: evaluate the trained agent + rerun real-LLM baselines on a
|
| 3 |
+
# wider per-type sample, then upload all results to the model repo.
|
| 4 |
+
|
| 5 |
+
set -euo pipefail
|
| 6 |
+
|
| 7 |
+
HF_USERNAME="${HF_USERNAME:-Dar3devil}"
|
| 8 |
+
PER_TYPE="${PER_TYPE:-4}"
|
| 9 |
+
ADAPTER_REPO="${ADAPTER_REPO:-${HF_USERNAME}/promptops-arena-agent}"
|
| 10 |
+
RESULTS_REPO="${ADAPTER_REPO}"
|
| 11 |
+
|
| 12 |
+
echo "[eval] HF_USERNAME=${HF_USERNAME} PER_TYPE=${PER_TYPE} ADAPTER_REPO=${ADAPTER_REPO}"
|
| 13 |
+
mkdir -p /workspace
|
| 14 |
+
cp -r /code/. /workspace/
|
| 15 |
+
cd /workspace
|
| 16 |
+
|
| 17 |
+
echo "[eval] python: $(python --version)"
|
| 18 |
+
nvidia-smi || echo "no nvidia-smi"
|
| 19 |
+
|
| 20 |
+
echo "[eval] installing deps"
|
| 21 |
+
pip install --no-cache-dir --upgrade pip
|
| 22 |
+
pip install --no-cache-dir \
|
| 23 |
+
"transformers==4.55.4" \
|
| 24 |
+
"peft==0.15.2" \
|
| 25 |
+
"accelerate==1.7.0" \
|
| 26 |
+
"datasets==3.6.0" \
|
| 27 |
+
"huggingface_hub>=0.25.0" \
|
| 28 |
+
"jsonschema>=4.20.0" \
|
| 29 |
+
"openenv-core>=0.1.0" \
|
| 30 |
+
"fastapi>=0.110.0" \
|
| 31 |
+
"uvicorn>=0.27.0" \
|
| 32 |
+
"pydantic>=2.0.0"
|
| 33 |
+
|
| 34 |
+
export PROMPTOPS_LLM_BACKEND=transformers
|
| 35 |
+
export PYTHONUTF8=1
|
| 36 |
+
export TOKENIZERS_PARALLELISM=false
|
| 37 |
+
|
| 38 |
+
mkdir -p outputs results
|
| 39 |
+
|
| 40 |
+
echo "[eval] downloading adapter ${ADAPTER_REPO}"
|
| 41 |
+
python - <<PY
|
| 42 |
+
from huggingface_hub import snapshot_download
|
| 43 |
+
import os
|
| 44 |
+
p = snapshot_download(repo_id="${ADAPTER_REPO}", repo_type="model",
|
| 45 |
+
local_dir="outputs/grpo-lora",
|
| 46 |
+
allow_patterns=["adapter_*", "*.json", "*.jinja", "*.txt", "training_log.jsonl"])
|
| 47 |
+
print("[eval] adapter at", p)
|
| 48 |
+
PY
|
| 49 |
+
|
| 50 |
+
echo "[eval] running zero-shot baseline (real LLM)"
|
| 51 |
+
python scripts/run_baseline.py --policy zero_shot --per-type "${PER_TYPE}" \
|
| 52 |
+
--out results/baseline_zero_shot_real.json
|
| 53 |
+
|
| 54 |
+
echo "[eval] running CoT baseline (real LLM)"
|
| 55 |
+
python scripts/run_baseline.py --policy cot --per-type "${PER_TYPE}" \
|
| 56 |
+
--out results/baseline_cot_real.json
|
| 57 |
+
|
| 58 |
+
echo "[eval] running trained-agent eval"
|
| 59 |
+
python scripts/eval_trained.py --adapter outputs/grpo-lora --per-type "${PER_TYPE}" \
|
| 60 |
+
--out results/trained_agent.json --max-turns 2
|
| 61 |
+
|
| 62 |
+
echo "[eval] uploading results to ${RESULTS_REPO}"
|
| 63 |
+
python - <<PY
|
| 64 |
+
import os
|
| 65 |
+
from huggingface_hub import HfApi
|
| 66 |
+
api = HfApi()
|
| 67 |
+
repo = "${RESULTS_REPO}"
|
| 68 |
+
for f in [
|
| 69 |
+
"results/baseline_zero_shot_real.json",
|
| 70 |
+
"results/baseline_cot_real.json",
|
| 71 |
+
"results/trained_agent.json",
|
| 72 |
+
]:
|
| 73 |
+
api.upload_file(path_or_fileobj=f, path_in_repo=os.path.basename(f),
|
| 74 |
+
repo_id=repo, repo_type="model",
|
| 75 |
+
commit_message=f"eval: {os.path.basename(f)}")
|
| 76 |
+
print("[eval] uploaded")
|
| 77 |
+
PY
|
| 78 |
+
|
| 79 |
+
echo "[eval] all done."
|