BudgetDoc is introduced, the first multimodal benchmark providing explicit supervision for model-budget-performance trade-offs across three document tasks, and DRB (Document-Reasoning Balancer), an approx.
Abstract
Uniformly allocating inference reasoning budgets to LLMs is expensive and prone to over-thinking penalties; especially in document tasks where visual layouts drive complexity. To address this, we introduce BudgetDoc, the first multimodal benchmark providing explicit supervision for model-budget-performance trade-offs across three document tasks. Using BudgetDoc, we train DRB (Document-Reasoning Balancer), an approx. 1B-parameter pre-flight estimator (SigLIP-2 + Qwen3-0.6B) that predicts ordinal model performance across budget levels, achieving a 0.753 weighted F1. When dynamically allocating reasoning budgets across five frontier models and three datasets, DRB matches or improves F1 scores compared to always-maximum-budget baselines in 9 of 15 configurations while drastically reducing cost. Finally, preliminary evaluations demonstrate DRB's potential to generalize to cross-model selection.
Treating supervision format as a first-class hyperparameter for multi-task reasoning SFT in large language models—at least in this benchmark-and-model setting—rather than a mere rendering detail is supported.
Nhat Thanh Vu, M. Rashid, Fariza Sabrina· Electronics· 0 citations
I Reasoning via Multi-Teacher Distillation is proposed, a novel framework that 'compiles' the reasoning abilities of large teacher LLMs into a lightweight student Small Language Model (SLM), which significantly outperforms state-of-the-art baselines in both recommendation accuracy and inference efficiency.
Experiments show that DIAG boosts yield across iterations and delivers stronger reasoning performance under an iso-effective training budget, demonstrating that it can distill more informative preference supervision for mathematical reasoning.
Gu-Han Chen, Songtao Tian, Bo-Han Li et al.· 0 citations
Standard evaluation of large language models is challenged by varying the token generation budget, i.e., the maximum tokens a model may produce, across seven levels, evaluating four models on three reasoning benchmarks, and finding three findings that argue for budget-conditioned evaluation protocols.
Rodrigo Guedes de Souza, Alison R. Panisson· 1 citation
The Powered Length Penalty (PLP) is proposed, an adaptive regularizer that penalizes redundancy in short sequences while gradually reducing penalties for longer sequences, preserving deep reasoning.
Ze-Hui Ling, De-Shu Chen, Hong-Wei Zhang et al.· Proceedings of the Thirty-Fi...· 0 citations
Reasoning-to-Encoder Distilla-tion (R2END), a framework that establishes a text encoder as the optimal student architecture for scalable recommendation, and compresses the teacher’s reasoning into a dense vector space via a semantic alignment objec-tive, effectively capturing user-item dynamics.
D. Han, Daeyoung Roh, A. Y. Kim et al.· Annual Meeting of the Associ...· 0 citations
