Cover Image

Learning to Trade Like an Expert: Cognitive Fine-Tuning for Stable Financial Reasoning in Language Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Pan, Yuchen, Liew, Soung Chang
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

GINO-Q: Learning an Asymptotically Optimal Index Policy for Restless Multi-armed Bandits
by: Chen, Gongpu, et al.
Published: (2024)

Scaling Laws for Upcycling Mixture-of-Experts Language Models
by: Liew, Seng Pei, et al.
Published: (2025)

Testing the Limits of Fine-Tuning for Improving Visual Cognition in Vision Language Models
by: Buschoff, Luca M. Schulze, et al.
Published: (2025)

Rephrase and Contrast: Fine-Tuning Language Models for Enhanced Understanding of Communication and Computer Networks
by: Wang, Liujianfu, et al.
Published: (2024)

ScoNe: Benchmarking Negation Reasoning in Language Models With Fine-Tuning and In-Context Learning
by: She, Jingyuan Selena, et al.
Published: (2023)

Towards Principled Design of Mixture-of-Experts Language Models under Memory and Inference Constraints
by: Liew, Seng Pei, et al.
Published: (2026)

Fine-Tuned Language Models Generate Stable Inorganic Materials as Text
by: Gruver, Nate, et al.
Published: (2024)

Learning Like Humans: Resource-Efficient Federated Fine-Tuning through Cognitive Developmental Stages
by: Wu, Yebo, et al.
Published: (2025)

Mixture of Cognitive Reasoners: Modular Reasoning with Brain-Like Specialization
by: AlKhamissi, Badr, et al.
Published: (2025)

Revisiting Privacy, Utility, and Efficiency Trade-offs when Fine-Tuning Large Language Models
by: Das, Soumi, et al.
Published: (2025)

Let the Expert Stick to His Last: Expert-Specialized Fine-Tuning for Sparse Architectural Large Language Models
by: Wang, Zihan, et al.
Published: (2024)

QuantLRM: Quantization of Large Reasoning Models via Fine-Tuning Signals
by: Zhang, Nan, et al.
Published: (2026)

State-offset Tuning: State-based Parameter-Efficient Fine-Tuning for State Space Models
by: Kang, Wonjun, et al.
Published: (2025)

PERFT: Parameter-Efficient Routed Fine-Tuning for Mixture-of-Expert Model
by: Liu, Yilun, et al.
Published: (2024)

Parameter-Efficient Fine-Tuning of State Space Models
by: Galim, Kevin, et al.
Published: (2024)

Measuring Fairness in Financial Transaction Machine Learning Models
by: Ayvaz, Deniz Sezin, et al.
Published: (2025)

CLEFT: Language-Image Contrastive Learning with Efficient Large Language Model and Prompt Fine-Tuning
by: Du, Yuexi, et al.
Published: (2024)

Adaptive LoRA Experts Allocation and Selection for Federated Fine-Tuning
by: Wang, Lei, et al.
Published: (2025)

MELINOE: Fine-Tuning Enables Memory-Efficient Inference for Mixture-of-Experts Models
by: Raje, Arian, et al.
Published: (2026)

A Stronger Mixture of Low-Rank Experts for Fine-Tuning Foundation Models
by: Sun, Mengyang, et al.
Published: (2025)

Fine-Tuning Language Models with Reward Learning on Policy
by: Lang, Hao, et al.
Published: (2024)

Reasoning Towards Fairness: Mitigating Bias in Language Models through Reasoning-Guided Fine-Tuning
by: Kabra, Sanchit, et al.
Published: (2025)

Embedding Enhancement via Fine-Tuned Language Models for Learner-Item Cognitive Modeling
by: Liu, Yuanhao, et al.
Published: (2026)

Unsupervised Identification and Removal of Spurious Correlations During Fine-Tuning
by: Gilligan-Lee, Ciarán M., et al.
Published: (2026)

Self-Generative Adversarial Fine-Tuning for Large Language Models
by: Wu, Shiguang, et al.
Published: (2026)

Diversity in Large Language Models under Supervised Fine-Tuning
by: Klypa, Roman, et al.
Published: (2026)

Decentralized Low-Rank Fine-Tuning of Large Language Models
by: Ghiasvand, Sajjad, et al.
Published: (2025)

Sparse Gradient Compression for Fine-Tuning Large Language Models
by: Yang, David H., et al.
Published: (2025)

Dynamic Scaled Gradient Descent for Stable Fine-Tuning for Classifications
by: Bui, Nghia, et al.
Published: (2026)

OD-MoE: On-Demand Expert Loading for Cacheless Edge-Distributed MoE Inference
by: Wang, Liujianfu, et al.
Published: (2025)

Adaptive and Fine-grained Module-wise Expert Pruning for Efficient LoRA-MoE Fine-Tuning
by: Li, Weihang, et al.
Published: (2026)

Reasoning-Trace Collapse: Evaluating the Loss of Explicit Reasoning During Fine-Tuning
by: Twist, Lukas, et al.
Published: (2026)

Enhancing Event Reasoning in Large Language Models through Instruction Fine-Tuning with Semantic Causal Graphs
by: Bethany, Mazal, et al.
Published: (2024)

Reinforcement Learning Fine-Tunes a Sparse Subnetwork in Large Language Models
by: Balashov, Andrii
Published: (2025)

A Risk-Aware Reinforcement Learning Reward for Financial Trading
by: Srivastava, Uditansh, et al.
Published: (2025)

TuckA: Hierarchical Compact Tensor Experts for Efficient Fine-Tuning
by: Lei, Qifeng, et al.
Published: (2025)

Linearization Explains Fine-Tuning in Large Language Models
by: Afzal, Zahra Rahimi, et al.
Published: (2026)

Continual Fine-Tuning of Large Language Models via Program Memory
by: Le, Hung, et al.
Published: (2026)

CANARY: Zero-Label Detection of Fine-Tuning Contamination in Language Models
by: Parekh, Swapnil
Published: (2026)

Matching Features, Not Tokens: Energy-Based Fine-Tuning of Language Models
by: Jelassi, Samy, et al.
Published: (2026)