Imagen de Portada

Are complicated loss functions necessary for teaching LLMs to reason?

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Carrino, Gabriele, Sassella, Andrea, Brunello, Nicolo, Toschi, Federico, Carman, Mark James
Formato:	Preprint
Publicado:	2026
Materias:	Machine Learning 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

From Instructions to Assistance: a Dataset Aligning Instruction Manuals with Assembly Videos for Evaluating Multimodal LLMs
por: Toschi, Federico, et al.
Publicado: (2026)

Benchmarking EngGPT2-16B-A3B against Comparable Italian and International Open-source LLMs
por: Sassella, Andrea, et al.
Publicado: (2026)

InTraVisTo: Inside Transformer Visualisation Tool
por: Brunello, Nicolò, et al.
Publicado: (2025)

Tag-LLM: Repurposing General-Purpose LLMs for Specialized Domains
por: Shen, Junhong, et al.
Publicado: (2024)

LLMs cannot find reasoning errors, but can correct them given the error location
por: Tyen, Gladys, et al.
Publicado: (2023)

QuestBench: Can LLMs ask the right question to acquire information in reasoning tasks?
por: Li, Belinda Z., et al.
Publicado: (2025)

QMOS: Enhancing LLMs for Telecommunication with Question Masked loss and Option Shuffling
por: Guda, Blessed, et al.
Publicado: (2024)

Language models show human-like content effects on reasoning tasks
por: Dasgupta, Ishita, et al.
Publicado: (2022)

When can transformers reason with abstract symbols?
por: Boix-Adsera, Enric, et al.
Publicado: (2023)

Artificial Expert Intelligence through PAC-reasoning
por: Shalev-Shwartz, Shai, et al.
Publicado: (2024)

Language hooks: a modular framework for augmenting LLM reasoning that decouples tool usage from the model and its prompt
por: de Mijolla, Damien, et al.
Publicado: (2024)

Is continuous CoT better suited for multi-lingual reasoning?
por: Bashir, Ali Hamza, et al.
Publicado: (2026)

Sudoku-Bench: Evaluating creative reasoning with Sudoku variants
por: Seely, Jeffrey, et al.
Publicado: (2025)

ContextGPT: Infusing LLMs Knowledge into Neuro-Symbolic Activity Recognition Models
por: Arrotta, Luca, et al.
Publicado: (2024)

Neural networks for abstraction and reasoning: Towards broad generalization in machines
por: Bober-Irizar, Mikel, et al.
Publicado: (2024)

Your thoughts tell who you are: Characterize the reasoning patterns of LRMs
por: Chen, Yida, et al.
Publicado: (2025)

To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning
por: Sprague, Zayne, et al.
Publicado: (2024)

Critique of Impure Reason: Unveiling the reasoning behaviour of medical Large Language Models
por: Sim, Shamus, et al.
Publicado: (2024)

Encode, Think, Decode: Scaling test-time reasoning with recursive latent thoughts
por: Koishekenov, Yeskendir, et al.
Publicado: (2025)

How do LLMs Compute Verbal Confidence
por: Kumaran, Dharshan, et al.
Publicado: (2026)

Hypertokens: Holographic Associative Memory in Tokenized LLMs
por: Augeri, Christopher James
Publicado: (2025)

Easy Problems That LLMs Get Wrong
por: Williams, Sean, et al.
Publicado: (2024)

Multi-step retrieval and reasoning improves radiology question answering with large language models
por: Wind, Sebastian, et al.
Publicado: (2025)

Explore Theory of Mind: Program-guided adversarial data generation for theory of mind reasoning
por: Sclar, Melanie, et al.
Publicado: (2024)

Learning and Enforcing Context-Sensitive Control for LLMs
por: Albinhassan, Mohammad, et al.
Publicado: (2026)

Interpretable Early Failure Detection via Machine Learning and Trace Checking-based Monitoring
por: Brunello, Andrea, et al.
Publicado: (2025)

Catching rationalization in the act: detecting motivated reasoning before and after CoT via activation probing
por: Mirtaheri, Parsa, et al.
Publicado: (2026)

BoostStep: Boosting mathematical capability of Large Language Models via improved single-step reasoning
por: Zhang, Beichen, et al.
Publicado: (2025)

Counterfactual reasoning: an analysis of in-context emergence
por: Miller, Moritz, et al.
Publicado: (2025)

Frictive Policy Optimization for LLMs: Epistemic Intervention, Risk-Sensitive Control, and Reflective Alignment
por: Pustejovsky, James, et al.
Publicado: (2026)

Draft-Conditioned Constrained Decoding for Structured Generation in LLMs
por: Reddy, Avinash, et al.
Publicado: (2026)

MarginSel : Max-Margin Demonstration Selection for LLMs
por: Ambati, Rajeev Bhatt, et al.
Publicado: (2025)

Zero-shot data citation function classification using transformer-based large language models (LLMs)
por: Byers, Neil, et al.
Publicado: (2025)

A Shared Geometry of Difficulty in Multilingual Language Models
por: Civelli, Stefano, et al.
Publicado: (2026)

Can formal argumentative reasoning enhance LLMs performances?
por: Castagna, Federico, et al.
Publicado: (2024)

How to Train Data-Efficient LLMs
por: Sachdeva, Noveen, et al.
Publicado: (2024)

Beyond Confidence: Rethinking Self-Assessments for Performance Prediction in LLMs
por: Bhattacharyya, Sree, et al.
Publicado: (2026)

Adapting Language Models via Token Translation
por: Feng, Zhili, et al.
Publicado: (2024)

UProp: Investigating the Uncertainty Propagation of LLMs in Multi-Step Agentic Decision-Making
por: Duan, Jinhao, et al.
Publicado: (2025)

Matryoshka Pilot: Learning to Drive Black-Box LLMs with LLMs
por: Li, Changhao, et al.
Publicado: (2024)