News / #training Tag Training 500 articles archived under #training · RSS Sign in to follow arXiv — Machine Learning research 28d ago Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion arXiv:2607.14371v1 Announce Type: new Abstract: Large Language Models (LLMs) have revolutionized AI services, but a critical tension emerges: while personalization improves model performance, it consumes scarce computational resources that users must share. When should a user… 26 arXiv — Machine Learning research 28d ago A Continuous-Time Reinforcement Learning Framework for Fine-Tuning Discrete Diffusion Models arXiv:2607.14522v1 Announce Type: new Abstract: We formulate reinforcement learning (RL) in continuous time with discrete state spaces and possibly arbitrary action spaces via a stochastic control approach, where the state dynamics are modeled as a controlled continuous-time… 29 arXiv — NLP / Computation & Language research 28d ago Innocuous-Seeming Data, Latent Ideology: Ideological Generalisation in Finetuned LLMs arXiv:2607.14888v1 Announce Type: cross Abstract: Finetuning language models on small, curated datasets is standard practice for adapting them to specific policies or domains. We show that finetuning on narrow, factually-defensible, moderation-passing data can cause broad… 13 arXiv — NLP / Computation & Language research 28d ago Introspection Fine-Tuning (IFT): Training Small LLMs to Introspect arXiv:2607.14111v1 Announce Type: new Abstract: Can small language models detect and report on perturbations their own internal activations? We investigate this question through the lens of activation steering: injecting concept vectors into a model's residual stream and… 33 arXiv — NLP / Computation & Language research 28d ago Expanding the Lexicon of Ge'ez Based African Languages: A Comparative Study of Amharic and Tigrinya arXiv:2607.15209v1 Announce Type: new Abstract: Multilingual pre-trained language models (PLMs) exhibit degraded performance on low-resource, non-Latin-script languages, driven by high out-of-vocabulary (OOV) rates and excessive subword fragmentation that result from… 32 arXiv — NLP / Computation & Language research 28d ago In-Place Tokenizer Expansion for Pre-trained LLMs arXiv:2607.15232v1 Announce Type: new Abstract: A tokenizer fixed at the start of pre-training allocates vocabulary in proportion to the pre-training corpus, reflecting the deployment priorities at that time. When those priorities shift, languages added later are split into many… 29 arXiv — NLP / Computation & Language research 28d ago Stop Thinking, Start Looking: Efficient Post-Training for Multimodal Document Question Answering via Reasoning-Free Alignment arXiv:2607.14682v1 Announce Type: cross Abstract: Efficient multimodal document question answering with explicit visual grounding, locating the precise document region that supports each answer remains an open challenge. Current approaches bifurcate into Supervised Fine-Tuning… 14 arXiv — NLP / Computation & Language research 28d ago Decoupled Alignment for Robust Plug-and-Play Adaptation arXiv:2406.01514v4 Announce Type: replace Abstract: We introduce a training-free safety enhancement method for aligning large language models (LLMs) without the need for supervised fine-tuning or reinforcement learning from human feedback. Our main idea is to provide a robust… 13 OpenAI Python SDK releases dev-tools 28d ago v2.46.0 2.46.0 (2026-07-17) Full Changelog: v2.45.0...v2.46.0 Features api: /organization/projects/{project_id}/service_accounts/{service_account_id}/api_keys" endpoint ( 5a00941 ) api: add owner_project_access to APIKeyListParams ( f589d04 ) api: manual updates ( 980f176 ) api: manual… 10 arXiv — Machine Learning research 29d ago EXPLORE: Exploration with Guided Search for Analog Topology Generation using Language Models arXiv:2607.13416v1 Announce Type: new Abstract: Automating analog circuit topology design is essential to reduce the extensive manual effort required to meet increasingly diverse and customized application demands. Recent advances have applied sequence-to-sequence fine-tuning on… 31 arXiv — Machine Learning research 29d ago MxGPS: Multiplex Graph Transformers for a Power Grid Foundation Model arXiv:2607.13763v1 Announce Type: new Abstract: Single-task fine-tuning of graph neural networks (GNNs) for power grid problems exhibits a systematic failure mode: models that achieve the lowest in-distribution error degrade the most under topology shift. We term this topology… 20 Hugging Face Daily Papers research 29d ago MAGIC: Transition-Aware Generation of Navigable Multi-Scene Game Worlds with Large Language Models Abstract Multi-scene navigation (clearing an objective in one bounded space and then crossing a portal into the next) is a defining feature of contemporary 3D games, but authoring it is laborious: every portal must have consistent endpoints on both sides, each interior must… 27 arXiv — Machine Learning research 1mo ago CARE-LoRA: Compressed Activation REconstruction for Memory-Efficient LoRA arXiv:2607.11940v1 Announce Type: new Abstract: As the scale of large pre-trained models continues to grow, fine-tuning them under limited memory budgets has become increasingly challenging. Low-Rank Adaptation (LoRA), currently one of the most widely adopted parameter-efficient… 24 arXiv — Machine Learning research 1mo ago Continual Learning with Elastic Regularization and Synthetic Replay for Federated MLLM Fine-Tuning arXiv:2607.12112v1 Announce Type: new Abstract: Federated fine-tuning of Multimodal Large Language Models (MLLMs) across distributed networks enables privacy-sensitive adaptation to evolving data streams, yet a fundamental obstacle prevents robust deployment in dynamic… 4 arXiv — Machine Learning research 1mo ago Verifier-Based Reinforcement Fine-Tuning of Reasoning Models for Thermal Energy Storage Control arXiv:2607.12856v1 Announce Type: new Abstract: Buildings are expected to shift cooling loads in response to grid conditions. Thermal energy storage (TES) enables this shift, but scheduling it well requires planning hours ahead under storage constraints. Model predictive control… 9 arXiv — NLP / Computation & Language research 1mo ago TAKE: Trajectory-Aware Knowledge Estimation for Text Dataset Distillation arXiv:2607.11898v1 Announce Type: new Abstract: Large-scale text corpora have become a quiet bottleneck in modern NLP, not just in storage, but in the accumulated cost of training, fine-tuning, and continual learning. We propose a text dataset distillation framework that reduces… 13 arXiv — NLP / Computation & Language research 1mo ago Transforming LLMs into Efficient Cross-Encoders via Knowledge Distillation for RAG Reranking arXiv:2607.11933v1 Announce Type: new Abstract: Cross-encoders achieve high reranking accuracy in Retrieval-Augmented Generation (RAG) pipelines but impose quadratic inference costs that limit real-time deployment. We address this by fine-tuning LLaMA 3 (8B) as a drop-in… 25 arXiv — NLP / Computation & Language research 1mo ago Entropy-Preserving Supervised Fine-Tuning via Adaptive Self-Distillation for Large Reasoning Models arXiv:2602.02244v3 Announce Type: replace-cross Abstract: The standard post-training recipe for large reasoning models, supervised fine-tuning followed by reinforcement learning (SFT-then-RL), may limit the benefits of the RL stage: while SFT imitates expert demonstrations, it… 6 arXiv — Machine Learning research 1mo ago Safe responses matter: Output-aware safety guardrail mitigate over-refusal in MLLMs arXiv:2607.09697v1 Announce Type: new Abstract: Existing safety mechanisms for multimodal large language models (MLLMs) face a fundamental trade-off between safety and utility. Model fine-tuning achieves robust safety but compromises general utility. Input-side safety guardrails… 16 arXiv — Machine Learning research 1mo ago Metadata-Free Meta-Reweighted Direct Preference Optimization under Noisy Preference Labels arXiv:2607.09796v1 Announce Type: new Abstract: Direct Preference Optimization (DPO) has become an important method for aligning large language models (LLMs) with human preferences because it removes the need for explicit reward modeling and reinforcement learning optimization.… 23 arXiv — Machine Learning research 1mo ago Conditional Optimal Bridge for Riemannian Activation Steering arXiv:2607.10517v1 Announce Type: new Abstract: Activation steering offers a lightweight alternative to fine-tuning for controlling large language models at inference time. While many existing methods implicitly optimize a log-density-ratio objective between desired and… 22 arXiv — Machine Learning research 1mo ago Learning to Fine-tune Foundation Models under Resource Limitations arXiv:2607.10694v1 Announce Type: new Abstract: We study the problem of optimal continual fine-tuning for a pre-trained Foundation Model deployed at a resource-limited device. At each time slot, a new batch of training data arrives, and the controller is faced with two options:… 22 arXiv — NLP / Computation & Language research 1mo ago Index SLM Technical Report arXiv:2607.09885v1 Announce Type: new Abstract: We present Index-1.9B, a series of open small language models developed at Bilibili. The series comprises four models: Index-1.9B-Base, a foundation model with 1.9 billion non-embedding parameters pre-trained on 2.8 trillion… 27 arXiv — NLP / Computation & Language research 1mo ago Unified Gradient Projection: Language-Balanced Continual Learning for Multilingual Low-Resource ASR arXiv:2607.11163v1 Announce Type: new Abstract: Large-scale pretrained ASR models such as Whisper exhibit strong multilingual capabilities. However, fine-tuning on low-resource languages often causes catastrophic forgetting. Although continual learning mitigates this issue,… 36 arXiv — NLP / Computation & Language research 1mo ago Beyond Sally-Anne: Evaluating Theory of Mind in LLMs using Epistemic Schelling Points arXiv:2607.11363v1 Announce Type: new Abstract: Text-based evaluations of Theory of Mind (ToM) in Large Language Models (LLMs) often involve cognitive tests akin to the Sally-Anne task that can be gamed due to exposure to relevantly similar tasks in pre-training and do not… 17 arXiv — NLP / Computation & Language research 1mo ago Direct Image-to-Modern Vietnamese Translation of Han-Nom Manuscripts via Multimodal RLHF Preference Alignment arXiv:2607.11434v1 Announce Type: new Abstract: Translating Han-Nom manuscripts into modern Vietnamese is challenging because historical pages are often degraded, the script contains rare logographic characters, and parallel supervision is limited. We propose a multimodal RLHF… 11 arXiv — NLP / Computation & Language research 1mo ago Improved Answer Selection with Pre-Trained Word Embeddings arXiv:1708.04326v1 Announce Type: cross Abstract: This paper evaluates existing and newly proposed answer selection methods based on pre-trained word embeddings. Word embeddings are highly effective in various natural language processing tasks and their integration into… 37 arXiv — NLP / Computation & Language research 1mo ago Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning arXiv:2607.09287v1 Announce Type: cross Abstract: Large language models (LLMs) remain expensive to fine-tune because full-parameter updates require substantial memory, compute, and per-task storage. We study whether saliency signals originally developed for pruning can be reused… 37 arXiv — NLP / Computation & Language research 1mo ago Mach-Mind-4-Flash Technical Report arXiv:2607.09375v1 Announce Type: cross Abstract: We present Mach-Mind-4-Flash, a 35B-parameter Mixture-of-Experts (MoE) agentic model with 3B activated parameters. Through post-training optimization alone without scaling pre-training compute, the model achieves performance on… 38 arXiv — Machine Learning research 1mo ago TheBioCollection: Unified Pre-Training Scale LLM Corpus for Biology arXiv:2607.08803v1 Announce Type: cross Abstract: The push toward large language models for biology (BioLM) has created a need for training corpora that can endow models with a genuine understanding of biology. However, existing biological resources, such as molecular databases,… 33 arXiv — NLP / Computation & Language research 1mo ago VTaMo: Video-Text Alignment Model for Sign Language Translation arXiv:2607.09126v1 Announce Type: cross Abstract: Sign language translation (SLT) converts continuous sign videos into spoken language text. Gloss-free approaches leverage pre-trained visual encoders and language models but rely on implicit cross-modal alignment from translation… 9 arXiv — NLP / Computation & Language research 1mo ago GrAInS: Gradient-based Attribution for Inference-Time Steering of LLMs and VLMs arXiv:2507.18043v2 Announce Type: replace Abstract: Inference-time steering methods offer a lightweight alternative to fine-tuning large language models (LLMs) and vision-language models (VLMs) by modifying internal activations at test time without updating model weights.… 11 Hugging Face Daily Papers research 1mo ago Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning Abstract Fine-tuning LLMs to inject new knowledge faces a critical challenge: LLMs can quickly memorize new facts, yet fail to use them for downstream reasoning tasks. We formalize this failure as the \textbf{Knowing--Using Gap}, characterized by an accuracy gap and a temporal… 20 r/LocalLLaMA community 1mo ago Why do people keep fine-tuning on summarized/censored SOTA CoT traces? Am I missing something? It seems like some people think distillation is magic and will raise the quality of output above what the base model is actually capable of. It's especially weird to me to see all these Fable fine-tunes, because as far as I understand it, they miss the… 18 r/LocalLLaMA community 1mo ago Toolnexus: a vendor-neutral tool-calling layer for LLMs, byte-identical across 5 languages (with real human-in-the-loop suspend/resume) Toolnexus is a small, vendor-neutral library that gives any LLM the dynamic tool-calling an agent framework has, but ported byte-identically across five languages (JavaScript, Python, Go, Java, C#). The idea: MCP servers, agent skills, your own functions, HTTP endpoints, the… 22 r/LocalLLaMA community 1mo ago actual advice about SLM fine tuning? hello real people and less-real bots, i'd appreciate if any of you people who have fine-tuned (either full or peft) more than half a model could share your wisdom about fine-tuning. i know i can ask the friendly neighborhood chatgpt and also unsloth has some detailed docs but… 24 r/LocalLLaMA community 1mo ago Training an LLM from scratch on 1800's texts (160GB dataset) Hi everyone, A year ago I began pre-training language models exclusively on 1800’s London data. Recently I have completed my largest dataset ever, containing 40B tokens or 160GB of 1800-1875 english data from England and the United States. I will soon train a 2B parameter model… 23 Hugging Face Daily Papers research 1mo ago DrugGen 2: A disease-aware language model for enhancing drug discovery Abstract DrugGen-2 generates small molecules conditioned on disease ontology and target protein sequences through fine-tuning GPT-2 with supervised learning and reinforcement learning using GRPO, achieving superior molecular diversity and binding affinity compared to baseline… 35 Hugging Face Daily Papers research 1mo ago LongE2V: Long-Horizon Event-based Video Reconstruction, Prediction, and Frame Interpolation with Video Diffusion Models Abstract LongE2V enables high-quality video recovery from sparse event streams by leveraging pre-trained video diffusion priors and addressing temporal stability and frame interpolation challenges. Generated by Qwen/Qwen2.5-Coder-32B-Instruct Recovering high-quality video from… 12 Hugging Face Daily Papers research 1mo ago Enhancing In-context Panoramic Generation via Geometric-aware Pretraining Abstract Canvas360 is a two-stage framework for in-context panoramic generation that combines geometry-aware pretraining with fine-tuning, featuring a large-scale dataset and novel modeling techniques for improved geometric consistency and global coherence. Generated by… 11 arXiv — Machine Learning research 1mo ago ReCoLoRA: Spectrum-Aware Recursive Consolidation for Continual LLM Fine-Tuning arXiv:2607.07719v1 Announce Type: new Abstract: Parameter-efficient fine-tuning adapts a large language model to one task cheaply, but across a task sequence LoRA-style methods keep stacking low-rank updates on the same frozen weight, so each new task tends to overwrite the… 31 arXiv — NLP / Computation & Language research 1mo ago DeepSearch-World: Self-Distillation for Deep Search Agents in a Verifiable Environment arXiv:2607.07820v1 Announce Type: new Abstract: Training tool-use agents to improve from their own experience remains challenging, as supervised fine-tuning relies on fixed teacher-distilled trajectories, while sparse-reward reinforcement learning provides weak supervision for… 28 arXiv — NLP / Computation & Language research 1mo ago TypeProbe: Recovering Type Representations from Hidden States of Pre-trained Code Models arXiv:2607.08339v1 Announce Type: new Abstract: State-of-the-art code models achieve impressive performance, yet the extent to which they internally encode type information remains poorly understood. We probe the residual streams of pretrained code models for internal type… 20 arXiv — NLP / Computation & Language research 1mo ago UltraX: Refining Pre-Training Data at Scale with Adaptive Programmatic Editing arXiv:2607.08646v1 Announce Type: new Abstract: As available training data approaches its physical limit, gains from Scaling Laws have begun to diminish. Consequently, improving Large Language Models (LLMs) now depends less on data expansion and more on higher-quality data… 33 arXiv — NLP / Computation & Language research 1mo ago Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning arXiv:2607.08393v1 Announce Type: cross Abstract: Fine-tuning LLMs to inject new knowledge faces a critical challenge: LLMs can quickly memorize new facts, yet fail to use them for downstream reasoning tasks. We formalize this failure as the \textit{\textbf{Knowing--Using Gap}},… 34 arXiv — NLP / Computation & Language research 1mo ago Peer-Predictive Self-Training for Language Model Reasoning arXiv:2604.13356v3 Announce Type: replace Abstract: Mechanisms for continued self-improvement of language models without external supervision remain an open challenge. We propose Peer-Predictive Self-Training (PST), a label-free fine-tuning framework in which multiple language… 4 arXiv — NLP / Computation & Language research 1mo ago DeepTutor: Towards Agentic Personalized Tutoring arXiv:2604.26962v3 Announce Type: replace-cross Abstract: Education is one of the most promising real-world applications for Large Language Models (LLMs). However, current LLMs rely on static pre-training knowledge and lack adaptation to individual learners, while existing RAG… 8 arXiv — NLP / Computation & Language research 1mo ago Wasserstein Distributionally Robust Regret Optimization for Reinforcement Learning from Human Feedback arXiv:2605.00155v3 Announce Type: replace-cross Abstract: Reinforcement learning from human feedback (RLHF) is a central post-training tool for aligning large language models, but its training reward is only a learned proxy for true human utility. This creates a decision problem… 10 NVIDIA Developer Blog official-blog 1mo ago Synthetic Data Generation for Financial AI Research with NVIDIA NeMo Fine-tuning LLMs for financial natural language processing (NLP) is constrained by limited, imbalanced data. Real-world financial news overrepresents earnings... 13 Simon Willison community 1mo ago llm 0.31.1 Release: llm 0.31.1 Fix for a bug with OpenAI Chat Completion endpoints where a tool call with empty arguments could result in a JSON error from some providers. #1521 This bug came up when I was testing llm-meta-ai . Tags: llm 31 Page 5 of 10 · 500 articles ← Newer Older →
