arXiv:2607. 19219v1 Announce Type: cross Abstract: Large language models (LLMs) have been widely applied to automated essay scoring (AES) and automated feedback generation (AFG).
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Large language models (LLMs) have been widely applied to automated essay scoring (AES) and automated feedback generation (AFG). However, existing studies rely primarily on prompt engineering or supervised fine-tuning, while systematic research on reinforcement learning (RL) post-training and automated evaluation of feedback quality remains limited.
The paper introduces HiFTS, a unified autoregressive framework that generates hierarchical chain-of-thought (CoT) feedback before predicting trait-level and holistic scores for multi-trait automated essay scoring. HiFTS distills rubric-grounded CoT feedback from a teacher large language model and trains student models to jointly produce feedback and scores, employing Group Relative Policy Optimization to balance score agreement, calibration, feedback quality, and structural validity. The authors also present CFMS-34, a new Chinese multi-trait AES dataset, and demonstrate that HiFTS achieves strong scoring performance while producing coherent, rubric-aligned feedback on CFMS-34 and ASAP++.
CriticGen introduces a generation‑aware evaluation framework that generates sample‑specific evaluation dimensions and scoring criteria across categories such as subjective, objective, and self‑derived constraints. These dynamic rubrics produce a score, reason, executable refinement suggestion, and a refined answer, enabling models to diagnose and target flaws in their responses. Experiments show significant gains in rubric quality, score correlation, and actionable feedback, with 73.17% of answers improved and a 93.28% non‑degradation rate.
The paper introduces a cost‑aware framework that treats each prompt type as an arm in a multi‑armed bandit controller, enabling adaptive selection of optimal prompting strategies during inference for automated essay scoring. Experiments on IELTS Writing Task 2 essays demonstrate that this bandit-driven approach achieves comparable scoring accuracy to exhaustive grid search while reducing LLM calls by 78.4%. The study also presents the first cost‑reliability learning curves for essay scoring, offering actionable insights for educational technology platforms balancing operational costs against assessment validity.
arXiv:2608.30005v1 Announce Type: new Abstract: Rubric-based reinforcement learning extends RL beyond tasks with exact answers or rule-based verifiers by scoring responses against instance-specific c...
Post-training of reasoning language models is commonly driven by supervised distillation and reinforcement learning with verifiable rewards. Distillation often relies on chain-of-thought annotations that are expensive to obtain and may themselves be noisy, incomplete, or partially incorrect; even when the final solution is correct, an imperfect rationale can interfere with learning.