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SlowGuess
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SlowGuess
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Qwen/Qwen3-8B
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This repository contains the RL-only ablation 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 applies rubric-guided GRPO directly to Qwen3-8B
for 200 updates with no SFT warm start, on a 1:1 mixture of the two tasks with each rollout
routed to its task's reward by data_source. It is the RL only row of the paper's
post-training ablation; the released model
ABForge-Qwen3-8B runs the same RL stage
from the SFT checkpoint instead.
| Resource | Link |
|---|---|
| Code | SlowGuess/Abforge_1 |
| Training & evaluation data | SlowGuess/abforge-data |
| Released model (SFT → GRPO) | SlowGuess/ABForge-Qwen3-8B |
| SFT checkpoint | SlowGuess/ABForge-Qwen3-8B-SFT |
| Per-paper outputs & judge rationales | outputs/task{1,2}/*/abforge-rl.jsonl in the data repo |
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_rl_task1 and in_rl_task2 — 30,000 papers each, disjoint from the SFT pool —
mixed 1:1. The benchmark papers carry no training flag, so they cannot leak in.
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 (base) | 44.4 | 43.4 |
| ABForge-Qwen3-8B-SFT (SFT only) | 30.7 | 52.2 |
| ABForge-Qwen3-8B-RL (this model, RL only) | 52.2 | 54.9 |
| ABForge-Qwen3-8B (SFT → GRPO) | 55.9 |
GRPO alone already lifts both tasks over the base model, and unlike SFT it does not trade one for the other. Warm-starting the same RL stage from the SFT checkpoint is still worth +3.7 on Task 1 and +7.5 on Task 2 — SFT is an effective RL initialization even though it does not improve Task 1 on its own.
Reproduce the numbers above with the code release:
bash
git clone https://github.com/SlowGuess/Abforge_1 && cd Abforge_1huggingface-cli download SlowGuess/abforge-data --repo-type dataset \--include "eval/*" --local-dir datapython run_inference_local.py --task 1 \--input data/eval/ablationbench_200.jsonl \--output outputs/task1_infer.jsonl \--model-path SlowGuess/ABForge-Qwen3-8B-RL \--dtype bf16 --device-map auto \--max-new-tokens 5120 --temperature 0.0 --stop-on '</Result>'export JUDGE_API_BASE=https://api.openai.com/v1export JUDGE_API_KEY=...export JUDGE_MODEL=...scripts/evaluate_task1.sh outputs/task1_infer.jsonl
Swap --task 2, --stop-on '</Proposed_Plan>' 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.
bibtex
@misc{abforge2026,title={ABForge: Post-Training for Paper-Grounded Ablation Design},author={TODO},year={2026},}
| 62.4 |