--- license: apache-2.0 language: - en base_model: - Qwen/Qwen3-8B datasets: - SlowGuess/abforge-data pipeline_tag: text-generation library_name: transformers tags: - ablation-study - scientific-reasoning - post-training - qwen3 --- # ABForge-Qwen3-8B-SFT

📄 ArXiv  ï½œ  💻 Code  ï½œ  🤗 Collection

## About This repository contains the **SFT stage** of **ABForge**, presented in *ABForge: Post-Training for Paper-Grounded Ablation Design*. Given a paper's methodology with its ablation content removed, ABForge proposes the ablation objectives the paper should investigate and designs a rigorous experiment plan for each — both from a single checkpoint. This model is [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) supervised fine-tuned for one full epoch on a 1:1 mixture of the two tasks, with no RL. It initializes the GRPO stage of [ABForge-Qwen3-8B](https://huggingface.co/SlowGuess/ABForge-Qwen3-8B) and is the *SFT only* row of the paper's post-training ablation. ## Links | Resource | Link | |---|---| | Code | [SlowGuess/Abforge_1](https://github.com/SlowGuess/Abforge_1) | | Training & evaluation data | [SlowGuess/abforge-data](https://huggingface.co/datasets/SlowGuess/abforge-data) | | Released model (SFT → GRPO) | [SlowGuess/ABForge-Qwen3-8B](https://huggingface.co/SlowGuess/ABForge-Qwen3-8B) | | RL-only checkpoint | [SlowGuess/ABForge-Qwen3-8B-RL](https://huggingface.co/SlowGuess/ABForge-Qwen3-8B-RL) | | Per-paper outputs & judge rationales | `outputs/task{1,2}/*/abforge-sft.jsonl` in the data repo | ## Training data [SlowGuess/abforge-data](https://huggingface.co/datasets/SlowGuess/abforge-data) ships a single table under `train/`, one row per paper, built by a semi-automated audit-in-the-loop pipeline over research papers from major ML, NLP and CV venues. This model trains on the rows flagged `in_sft_task1` (45,961 papers) and `in_sft_task2` (36,955), shuffled into a 1:1 task mixture for one full epoch. The benchmark papers carry no training flag, so they cannot leak in. ## Performance AblationBench, automated rubric-based LLM-as-a-Judge evaluation (`eval/ablationbench_200.jsonl`, 200 papers, judge `claude-sonnet-4-6`). Task 1 is ablation objective identification (`paper_score`); Task 2 is ablation plan synthesis (`design_score`, ×100). | Model | Task 1 | Task 2 | |---|---|---| | [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) (base) | 44.4 | 43.4 | | **ABForge-Qwen3-8B-SFT** (this model, SFT only) | **30.7** | **52.2** | | [ABForge-Qwen3-8B-RL](https://huggingface.co/SlowGuess/ABForge-Qwen3-8B-RL) (RL only) | 52.2 | 54.9 | | [ABForge-Qwen3-8B](https://huggingface.co/SlowGuess/ABForge-Qwen3-8B) (SFT → GRPO) | 55.9 | 62.4 | > SFT alone lifts Task 2 but costs Task 1 relative to the base model. Its value is as an RL > initialization: GRPO started from here beats GRPO started from the base model by +3.7 on > Task 1 and +7.5 on Task 2. ## Evaluation Reproduce the numbers above with the [code release](https://github.com/SlowGuess/Abforge_1): ```bash git clone https://github.com/SlowGuess/Abforge_1 && cd Abforge_1 huggingface-cli download SlowGuess/abforge-data --repo-type dataset \ --include "eval/*" --local-dir data python run_inference_local.py --task 1 \ --input data/eval/ablationbench_200.jsonl \ --output outputs/task1_infer.jsonl \ --model-path SlowGuess/ABForge-Qwen3-8B-SFT \ --dtype bf16 --device-map auto \ --max-new-tokens 5120 --temperature 0.0 --stop-on '' export JUDGE_API_BASE=https://api.openai.com/v1 export JUDGE_API_KEY=... export JUDGE_MODEL=... scripts/evaluate_task1.sh outputs/task1_infer.jsonl ``` Swap `--task 2`, `--stop-on ''` and `scripts/evaluate_task2.sh` for Task 2. The model is trained on the prompt templates in the code release and the rubric evaluator expects the matching output structure, so use those templates and greedy decoding. ## Citation ```bibtex @misc{abforge2026, title={ABForge: Post-Training for Paper-Grounded Ablation Design}, author={TODO}, year={2026}, } ```