autodata-policy-cs

A Qwen2.5-0.5B-Instruct policy fine-tuned with GRPO (Group Relative Policy Optimization) on synthetic CS-reasoning data generated by Autodata Studio — an implementation of the agentic self-instruct loop from Autodata: An agentic data scientist to create high-quality synthetic data (arXiv:2606.25996v2).

This is a testing-phase / infrastructure-validation run, not a production model. Its purpose was to prove the full pipeline end to end on real hardware.

What this run proved

Stage	Result
72B challenger generates calibrated CS questions	✅ 5/8 source docs produced a real weak/strong gap (30–60 pts)
Curated data pushed to the Hub	✅ ligaments-dev/autodata-grpo-cs
GRPO training on HF Jobs (A10G, 24GB)	✅ 100 steps, LoRA, programmatic reward, ~17 min
Trained adapter pushed to the Hub	✅ this repo

Honest limitations of this run

• Reward did not improve. Mean reward oscillated around 0.47–0.55 across all 100 steps (no upward trend). The model was trained, but not measurably improved.
• Root cause: completions/clipped_ratio = 1.0 — every generation hit the 256-token cap and never emitted a stop token, so the token-overlap reward stayed ~constant and GRPO had no usable gradient.
• Tiny dataset: only 5 prompts → 20 epochs of overfitting, no generalization signal.

What a real (improving) run needs

1. More data — hundreds of accepted prompts, not 5.
2. Fix completion termination — investigate why EOS is never emitted; raise max_completion_length and/or correct the chat/generation config.
3. A richer reward — swap the lexical-overlap proxy for the paper's rubric/LLM-judge reward, or add a stop-token / brevity shaping term.
4. Scale the GPU — move from a10g-small to a100-large once the dataset and reward are sound.

Training configuration

• Base model: Qwen/Qwen2.5-0.5B-Instruct
• Method: GRPO + LoRA (r=16, alpha=32, q/k/v/o projections)
• Reward: token-F1 overlap vs. reference answer + length/format shaping (programmatic, no API)
• Steps: 100, lr 1e-5, num_generations 8, max_completion_length 256, bf16
• Hardware: 1× A10G (24 GB) via HF Jobs

Usage

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
model = PeftModel.from_pretrained(base, "ligaments-dev/autodata-policy-cs")
tok = AutoTokenizer.from_pretrained("ligaments-dev/autodata-policy-cs")