[Paper] EDIT: Evidence-Diagnosed Intervention Training for Rule-Faithful LLM Grading
Source: arXiv - 2606.06350v1
Overview
Reliable rubric grading requires more than accurate score prediction. Each judgement must be grounded in the mark scheme and evidence from the student answer. Existing credit-assignment and intervention methods, primarily designed for self-contained reasoning tasks such as mathematics reasoning, struggle in this setting because they do not identify where grading reasoning goes wrong or how the model’s belief about the final mark changes during reasoning. We propose Evidence-Diagnosed Intervention Training (EDIT), a two-phase framework for training more rubric-faithful LLM graders. First, EDIT-SFT locates problematic reasoning steps using internal model signals: posterior belief over the final mark and input-grounding scores. It then revises only these local steps with help from a rubric checklist. Second, EDIT-RL calibrates the grader with belief-guided reward shaping, penalising large harmful belief drifts while still allowing helpful exploration. Experiments on two real-world, multi-subject grading benchmarks demonstrate that EDIT consistently outperforms strong supervised fine-tuning and reinforcement learning baselines on both in-domain and out-of-domain splits, with ablation studies confirming that internal-state diagnostics drive these gains.
Key Contributions
This paper presents research in the following areas:
- cs.CL
Methodology
Please refer to the full paper for detailed methodology.
Practical Implications
This research contributes to the advancement of cs.CL.
Authors
- Zhihao Wu
- Linhai Zhang
- Taiyi Wang
- Runcong Zhao
- Peter Andrews
- Cesare Aloisi
- Yulan He
Paper Information
- arXiv ID: 2606.06350v1
- Categories: cs.CL
- Published: June 4, 2026
- PDF: Download PDF