[2502.13604] BeamLoRA: Beam-Constraint Low-Rank Adaptation
![[2502.13604] BeamLoRA: Beam-Constraint Low-Rank Adaptation](https://i0.wp.com/arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png?w=1920&resize=1920,1113&ssl=1 "[2502.13604] BeamLoRA: Beam-Constraint Low-Rank Adaptation"){kind=logo}
[Submitted on 19 Feb 2025 (v1), last revised 12 Jun 2025 (this version, v2)] View a PDF of the paper ...
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[2411.18553] Retrofitting Large Language Models with Dynamic Tokenization
[Submitted on 27 Nov 2024 (v1), last revised 11 Jun 2025 (this version, v3)] View a PDF of the paper ...
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[2410.11005] Assessing Dialect Fairness and Robustness of Large Language Models in Reasoning Tasks
[Submitted on 14 Oct 2024 (v1), last revised 9 Jun 2025 (this version, v3)] View a PDF of the paper ...
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Parallel Long-Context Compressor for Length Extrapolation
[Submitted on 20 Feb 2025 (v1), last revised 9 Jun 2025 (this version, v2)] View a PDF of the paper ...
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[2412.17451] Diving into Self-Evolving Training for Multimodal Reasoning
[Submitted on 23 Dec 2024 (v1), last revised 6 Jun 2025 (this version, v3)] View a PDF of the paper ...
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[2506.05316] Improving Data Efficiency for LLM Reinforcement Fine-tuning Through Difficulty-targeted Online Data Selection and Rollout Replay
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals ...
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Advancing LLM Reasoning with Natural Language and Numerical Feedback
[Submitted on 3 Jun 2025 (v1), last revised 4 Jun 2025 (this version, v2)] View a PDF of the paper ...
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Tone recognition in low-resource languages of North-East India: peeling the layers of SSL-based speech models
arXiv:2506.03606v1 Announce Type: cross Abstract: This study explores the use of self-supervised learning (SSL) models for tone recognition in three ...
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[2505.23809] LLM-Driven E-Commerce Marketing Content Optimization: Balancing Creativity and Conversion
[Submitted on 27 May 2025 (v1), last revised 3 Jun 2025 (this version, v2)] View a PDF of the paper ...
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Replacing Re-ranking with Selection in RAG for Sensitive Domains
[Submitted on 21 May 2025 (v1), last revised 3 Jun 2025 (this version, v3)] View a PDF of the paper ...
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