Imagen de Portada

Warm Up Before You Train: Unlocking General Reasoning in Resource-Constrained Settings

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Shrestha, Safal, Kim, Minwu, Nepal, Aadim, Shrestha, Anubhav, Ross, Keith
Formato:	Preprint
Publicado:	2025
Materias:	Artificial Intelligence Computation and Language
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Reinforcement Learning vs. Distillation: Understanding Accuracy and Capability in LLM Reasoning
por: Kim, Minwu, et al.
Publicado: (2025)

On the Limits of Layer Pruning for Generative Reasoning in Large Language Models
por: Shrestha, Safal, et al.
Publicado: (2026)

Training Reasoning Models on Saturated Problems via Failure-Prefix Conditioning
por: Kim, Minwu, et al.
Publicado: (2026)

Layer Importance for Mathematical Reasoning is Forged in Pre-Training and Invariant after Post-Training
por: Nepal, Aadim, et al.
Publicado: (2025)

Mathematical Reasoning in Large Language Models: Assessing Logical and Arithmetic Errors across Wide Numerical Ranges
por: Shrestha, Safal, et al.
Publicado: (2025)

Nwāchā Munā: A Devanagari Speech Corpus and Proximal Transfer Benchmark for Nepal Bhasha ASR
por: Sharma, Rishikesh Kumar, et al.
Publicado: (2026)

Efficient Multi-Hop Question Answering over Knowledge Graphs via LLM Planning and Embedding-Guided Search
por: Shrestha, Manil, et al.
Publicado: (2025)

Conformal Prediction for Risk-Controlled Medical Entity Extraction Across Clinical Domains
por: Shrestha, Manil, et al.
Publicado: (2026)

Towards Nepali-language LLMs: Efficient GPT training with a Nepali BPE tokenizer
por: Shrestha, Adarsha, et al.
Publicado: (2025)

Scaling Up RL: Unlocking Diverse Reasoning in LLMs via Prolonged Training
por: Liu, Mingjie, et al.
Publicado: (2025)

Visual Grounding Methods for VQA are Working for the Wrong Reasons!
por: Shrestha, Robik, et al.
Publicado: (2020)

Stop Before You Fail: Operational Capability Boundaries for Mitigating Unproductive Reasoning in Large Reasoning Models
por: Zhang, Qingjie, et al.
Publicado: (2025)

Think Before You Lie: How Reasoning Leads to Honesty
por: Yuan, Ann, et al.
Publicado: (2026)

SnapKV: LLM Knows What You are Looking for Before Generation
por: Li, Yuhong, et al.
Publicado: (2024)

Think Twice Before You Write -- an Entropy-based Decoding Strategy to Enhance LLM Reasoning
por: He, Jiashu, et al.
Publicado: (2026)

Verify Before You Commit: Towards Faithful Reasoning in LLM Agents via Self-Auditing
por: Yuan, Wenhao, et al.
Publicado: (2026)

Look Before You Leap: Problem Elaboration Prompting Improves Mathematical Reasoning in Large Language Models
por: Liao, Haoran, et al.
Publicado: (2024)

ALIGN: Word Association Learning for Cultural Alignment in Large Language Models
por: Liu, Chunhua, et al.
Publicado: (2025)

Difficulty Estimation and Simplification of French Text Using LLMs
por: Jamet, Henri, et al.
Publicado: (2024)

CALM Before the STORM: Unlocking Native Reasoning for Optimization Modeling
por: Tang, Zhengyang, et al.
Publicado: (2025)

Evaluation of Language Models in the Medical Context Under Resource-Constrained Settings
por: Posada, Andrea, et al.
Publicado: (2024)

Look Before You Leap: Autonomous Exploration for LLM Agents
por: Ye, Ziang, et al.
Publicado: (2026)

A Survey on LLM-Assisted Clinical Trial Recruitment
por: Ghosh, Shrestha, et al.
Publicado: (2025)

Think Before You Prune: Self-Reflective Structured Pruning for Reasoning Language Models
por: Wang, Ziyan, et al.
Publicado: (2025)

Watch Before You Answer: Learning from Visually Grounded Post-Training
por: Zhang, Yuxuan, et al.
Publicado: (2026)

NepaliGPT: A Generative Language Model for the Nepali Language
por: Pudasaini, Shushanta, et al.
Publicado: (2025)

Token-Driven GammaTune: Adaptive Calibration for Enhanced Speculative Decoding
por: Gautam, Aayush, et al.
Publicado: (2025)

Summarize Before You Speak with ARACH: A Training-Free Inference-Time Plug-In for Enhancing LLMs via Global Attention Reallocation
por: Wang, Jingtao, et al.
Publicado: (2026)

Unlocking Anticipatory Text Generation: A Constrained Approach for Large Language Models Decoding
por: Tu, Lifu, et al.
Publicado: (2023)

Before You Interpret the Profile: Validity Scaling for LLM Metacognitive Self-Report
por: Cacioli, Jon-Paul
Publicado: (2026)

Thinking Before Constraining: A Unified Decoding Framework for Large Language Models
por: Nguyen, Ngoc Trinh Hung, et al.
Publicado: (2026)

Read Before You Think: Mitigating LLM Comprehension Failures with Step-by-Step Reading
por: Han, Feijiang, et al.
Publicado: (2025)

Do Before You Judge: Self-Reference as a Pathway to Better LLM Evaluation
por: Lin, Wei-Hsiang, et al.
Publicado: (2025)

Think Thrice Before You Act: Progressive Thought Refinement in Large Language Models
por: Du, Chengyu, et al.
Publicado: (2024)

Enabling LLM Knowledge Analysis via Extensive Materialization
por: Hu, Yujia, et al.
Publicado: (2024)

Mining the Mind: What 100M Beliefs Reveal About Frontier LLM Knowledge
por: Ghosh, Shrestha, et al.
Publicado: (2025)

Look Before You Leap: Enhancing Attention and Vigilance Regarding Harmful Content with GuidelineLLM
por: Zhang, Shaoqing, et al.
Publicado: (2024)

Do LLMs Need Inherent Reasoning Before Reinforcement Learning? A Study in Korean Self-Correction
por: Kim, Hongjin, et al.
Publicado: (2026)

Think Before You Act: Decision Transformers with Working Memory
por: Kang, Jikun, et al.
Publicado: (2023)

Unlocking the Potential of Model Merging for Low-Resource Languages
por: Tao, Mingxu, et al.
Publicado: (2024)