STAR arm A5 (seed 42) — LoRA adapter

Authors: Chaitanya Medidar, Manasi Ghalsasi, Pouras Patil, Rushil Patil (Vivekanand Education Society's Institute of Technology, Mumbai)

Part of STAR: Execution-Grounded, Typed Recovery Supervision for Coding-Agent SFT. Code, paper materials and manifests: https://github.com/chaitanyamedidar/STAR

Arm A5: STAR: T4 recovery spans force-kept + median base-loss cut on the rest
Base model: Qwen/Qwen3-8B (revision b968826d9c46dd6066d109eabc6255188de91218), not included; LoRA r=64, alpha=128, all attention+MLP projections.
Seed: 42. The training run manifest is run_manifest.json. Dataset (adapters, weights, labels, records): pourasrp/star_v1, DOI 10.57967/hf/11003. Per-token weight files are also in pourasrp/star-weights.

file	sha256
adapter_model.safetensors	`144ad8681ad879eefc0495ea305cc70b432bce26e260ee985ea2554b59590056`
adapter_config.json	`ee6070f2ae37e6c3f9e89e7cbeb7c2f5b30af87e141b32735b18fcbb668313be`

Verify with sha256sum adapter_model.safetensors.

from peft import PeftModel; from transformers import AutoModelForCausalLM
m = PeftModel.from_pretrained(AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-8B"), "pourasrp/star-A5")

License: Apache-2.0 (matches the GitHub repo). Training data came from SWE-Lego Real. No trajectory text is redistributed here.

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