News / #training Tag Training 500 articles archived under #training · RSS Sign in to follow r/LocalLLaMA community 1mo ago Reasoning-Medical0.1-27B (Qwen3.5-27B medical finetune, claims to surpass MedGemma)   submitted by   /u/beneath_steel_sky [link]   [comments] 35 r/LocalLLaMA community 1mo ago Modded RTX 4090 48GB vs Radeon AI Pro R9700 vs Arc Pro B70 for local coding LLMs? Building a personal rig mainly for running coding LLMs locally (inference,maybe light fine-tuning). Already have the motherboard/rest of the platform sorted — just deciding on the GPU. Three options I keep coming back to: Modded RTX 4090 48GB (Chinese clamshell mod) — I have an… 11 arXiv — Machine Learning research 1mo ago Online Data Selection Is Implicit Alignment arXiv:2607.07023v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) is often treated as a capability-adaptation step, while alignment is attributed to later preference optimization or reinforcement learning. This separation is incomplete: when examples are scored and… 33 arXiv — NLP / Computation & Language research 1mo ago Riemannian Geometry for Pre-trained Language Model Embeddings arXiv:2607.07047v1 Announce Type: new Abstract: Understanding the geometric structure of pre-trained language model embeddings matters for interpretability and safety. We ask whether sentence-level classification signal lives in the Riemannian geometry of contextual token… 21 arXiv — NLP / Computation & Language research 1mo ago TF-Engram: A Train-Free Engram with SSD-Backed Memory for Large Language Models arXiv:2607.07388v1 Announce Type: new Abstract: Large Language Models (LLMs) store factual knowledge and domain-specific patterns implicitly in dense Transformer parameters, making knowledge expansion costly through pretraining, fine-tuning, retrieval augmentation, or longer… 38 arXiv — NLP / Computation & Language research 1mo ago SynthAVE: Scalable Synthetic Labeling for E-Commerce with LLM-Arena Validation arXiv:2607.07469v1 Announce Type: new Abstract: Fine-tuning large language models (LLMs) for e-commerce attribute extraction requires labeled data representative across thousands of product types, attributes, and multiple languages. This combinatorial scale translates to… 17 r/LocalLLaMA community 1mo ago any one else finds Mimo v2.5 better than deepseek v4 flash!? I noticed while using both, mimo was often better, after benchmarking mimo v2.5 via open code endpoint in diff harness like codex, oh my pi, hermes. i found that mimo is indeed better in coding tasks. and over all, hermes scored 55% with mimo v2.5 via terminal bench v2.0 others… 25 arXiv — Machine Learning research 1mo ago A Coin Flip Per Token: Bernoulli Sparse Steering of Large Language Models arXiv:2607.05615v1 Announce Type: new Abstract: Activation steering via sparse autoencoders (SAEs) enables behavioral control of large language models without task-specific fine-tuning, but standard methods apply the steering signal at every generated token, incurring constant… 28 arXiv — Machine Learning research 1mo ago x-Prediction Is All You Need:Training-Free Accelerated Generation via Endpoint Decodability arXiv:2607.06114v1 Announce Type: new Abstract: Diffusion and flow matching models generate high-quality samples, but their ODE samplers often need tens to hundreds of neural function evaluations (NFEs). This remains a practical challenge for released checkpoints, since many… 22 arXiv — Machine Learning research 1mo ago Life Cycle Assessment of Pre-training the Lucie 7B Open-Source Large Language Model on the Jean Zay Supercomputer arXiv:2607.05408v1 Announce Type: cross Abstract: The environmental impact of training large language models (LLMs) is increasingly scrutinised, yet most published estimates focus on operational energy and disclose little about manufacturing (embodied) emissions, water… 13 arXiv — Machine Learning research 1mo ago Energy-Efficient GPU DVFS for Fine-Tuning of SLMs on Resource-constrained Embedded Devices arXiv:2607.05933v1 Announce Type: cross Abstract: Dynamic Voltage Frequency Scaling (DVFS) on resource-constrained embedded GPU platforms is essential for energy-efficient small language model (SLM) fine-tuning, as privacy- and personalization-driven adaptation increasingly… 17 arXiv — NLP / Computation & Language research 1mo ago Is Domain Adaptation Always Helpful? A Frozen-Backbone Study of Cross-Domain Sentiment Transfer arXiv:2607.05937v1 Announce Type: new Abstract: Sentiment analysis with frozen pre-trained language model (PLM) backbones has become a common paradigm, yet the practical benefit of explicit domain adaptation remains unclear, particularly when backbones encode varying degrees of… 18 arXiv — NLP / Computation & Language research 1mo ago LongCrafter: Towards Diverse Long-Context Understanding via Evidence-Graph-Guided Instruction Synthesis arXiv:2607.06160v1 Announce Type: new Abstract: Synthesizing long-context supervised fine-tuning (SFT) data is a scalable way to enhance the long-context understanding of large language models (LLMs), yet existing approaches share three limitations: narrow task coverage,… 10 arXiv — NLP / Computation & Language research 1mo ago Improving LLM-Generated Process Model Quality Through Reinforcement Learning: The Role of Reward Function Design arXiv:2607.06175v1 Announce Type: new Abstract: Large language models (LLMs) can generate BPMN process models from natural-language descriptions, yet supervised fine-tuning (SFT) limits their output quality to the patterns present in the training data. Reinforcement learning… 4 r/MachineLearning community 1mo ago What if a model could only learn what trusted LoRA adapters can express? [R] Hello I published a paper. Most defenses against fine-tuning poisoning try to detect malicious data or reduce its impact. I explored a different question: What if the model simply could not learn certain malicious updates? The idea is to constrain fine-tuning to a subspace… 31 Hugging Face Daily Papers research 1mo ago SynCity 3000: Bootstrapping Scene-Scale 3D Diffusion Abstract SynCity 3000 generates large, coherent 3D scenes by adapting image-to-3D generators as convolutional operators through fine-tuning on synthetic scene data. Generated by Qwen/Qwen2.5-Coder-32B-Instruct We present SynCity 3000, a framework for generating 3D scenes that… 13 Hugging Face Daily Papers research 1mo ago LLM-as-a-Verifier: A General-Purpose Verification Framework Abstract LLM-as-a-Verifier introduces a probabilistic verification framework that scales across multiple dimensions to improve solution correctness assessment and agent performance across various benchmarks. Generated by Qwen/Qwen2.5-Coder-32B-Instruct Scaling pre-training,… 21 r/MachineLearning community 1mo ago [D] Issue with arxiv - abstract not matching pdf/html [D] Hi, I was reading the openRLHF paper: https://arxiv.org/pdf/2501.03262v4 , but when I click the abstract page: https://arxiv.org/abs/2501.03262v4 , it shows "REINFORCE++". Note that https://arxiv.org/html/2501.03262v4 still shows the correct openRLHF paper. I believe Arxiv is… 19 arXiv — Machine Learning research 1mo ago Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting arXiv:2607.02637v1 Announce Type: new Abstract: Recent generative models can produce high-quality synthetic images, offering scalable training training data for data-hungry models. Existing approaches to exploiting this potential typically involve 1) training or fine-tuning… 9 arXiv — Machine Learning research 1mo ago Labeled-Data-Free Meta-Learning: Efficient Task Generation Using Pre-trained Models and Unlabeled Data arXiv:2607.02850v1 Announce Type: new Abstract: Meta-learning without labeled data is crucial for real-world applications, where obtaining labeled datasets can be expensive or restricted due to privacy concerns. Data-Free Meta-Learning (DFML) addresses this challenge by… 22 arXiv — Machine Learning research 1mo ago Can Model Merging Improve Aggregation in DiLoCo? arXiv:2607.03011v1 Announce Type: new Abstract: Model merging techniques, which aggregate independently finetuned models into one to combine their capabilities, have become a topic of significant interest in recent years, with a broad array of methods having been proposed to… 28 arXiv — Machine Learning research 1mo ago Transition Information Density: Morphological Trajectories, Synesthetic Perception, and Structured Interpolation in Neural Training (or: The Synesthetic AI) arXiv:2607.03210v1 Announce Type: new Abstract: Standard machine learning training presents data as discrete endpoint pairs, omitting the structure of the space between them. This paper introduces Transition Information Density (TID) -- the information content recoverable from… 29 arXiv — Machine Learning research 1mo ago Co-Adaptive Multi-Task LoRA: Transfer-Aware, Label-Free Control of Domain Participation arXiv:2607.03522v1 Announce Type: new Abstract: Fine-tuning a single low-rank adapter on many domains at once is multi-task learning: the domains must be co-learned, and how they share the adapter decides whether they help or hurt one another. Most efficient fine-tuning… 28 arXiv — Machine Learning research 1mo ago Punching Above Their Weight: Classification-Head Fine-Tuning of Tiny Language Models (TLMs) for Verifiable Multiple-Choice Tasks arXiv:2607.03801v1 Announce Type: new Abstract: We define Tiny Language Models (TLMs) as models below roughly 3B parameters that fit on mainstream consumer devices. We study how to adapt them for and use them on verifiable multiple-choice tasks. We compare three LoRA-based… 36 arXiv — NLP / Computation & Language research 1mo ago Revealing Hidden Model Behaviors with Task-Specific Self-Reports arXiv:2607.03640v1 Announce Type: new Abstract: Fine-tuning can give a language model a hidden behavior--it may give false answers under a narrow condition, or give harmful advice only when a prompt touches a particular topic. We introduce the Stabilized Adapter for self-Report… 29 arXiv — NLP / Computation & Language research 1mo ago Transplanting, inverting, and preventing a misalignment persona: method-conditional emergent misalignment in Qwen2.5 arXiv:2607.04510v1 Announce Type: new Abstract: Emergent misalignment (EM) -- the broad misbehaviour a language model acquires after fine-tuning on narrow harmful data -- is mediated in Qwen2.5 models by a latent persona direction, and that direction is causal in open weights.… 7 arXiv — NLP / Computation & Language research 1mo ago What You See Is What You Get: Observation-Aligned Supervision for Chart-to-Code Generation arXiv:2607.04726v1 Announce Type: new Abstract: Chart-to-code generation is commonly trained with supervised fine-tuning on reference plotting scripts, implicitly treating the gold code as a fully observable target. We argue that this assumption is often invalid: many chart… 4 arXiv — NLP / Computation & Language research 1mo ago LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure arXiv:2607.04733v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) is the standard approach for adapting pretrained language models to downstream domains, yet it often improves target-domain behavior at the cost of degrading pre-existing capabilities. Standard… 11 r/LocalLLaMA community 1mo ago UPDATE: I built a tool to turn your Claude Code sessions into fine-tuning data for local models (You can now convert your Codex and Pi sessions) A few days ago I shared this resource I created to convert your Claude Code sessions into training data (Thank you so much for all the support :D ): Original Post Today I'm sharing that I just released version 1.5.0, which now supports converting your Claude Code, Codex, and Pi… 13 r/MachineLearning community 1mo ago If your GPU can run inference, it should be able to fine-tune too. [P] I spent the last few months building a new sparse fine-tuning method for MoE models called **USAF**. The goal was simple: if your GPU can run inference on an MoE model, it should also be able to fine-tune it. On my AMD RX 6750 XT (12 GB), I can fine-tune Qwen3-30B-A3B by… 17 r/LocalLLaMA community 1mo ago google/tabfm-1.0.0 TabFM is a zero-shot tabular foundation model from Google Research. It supports classification and regression on structured/tabular data with mixed numerical and categorical columns, requiring no fine-tuning or hyperparameter search - training examples are passed as context and… 30 r/MachineLearning community 1mo ago Contrastive Decoding Diffing (CDD): recovering verbatim finetuning data from logits alone, no weight access needed[R] We built a model diffing method that recovers verbatim content from narrowly finetuned LLMs using only grey-box logit access (no weights, no activations, no probe corpus). Recent work (Minder, Dumas et al., "Narrow Finetuning Leaves Clearly Readable Traces in Activation… 12 r/LocalLLaMA community 1mo ago Portugal just released their own LLM Amalia (9B)! I didnt see any mention here. Source: https://portugal.gov.pt/en/gc25/communication/news/llm-amalia-shows-portugals-potential HF link SFT: https://huggingface.co/amalia-llm/AMALIA-9B-0626-SFT HF link DFO (Direct Preference Optimization): amalia-llm/AMALIA-9B-0626-DPO · Hugging… 15 r/MachineLearning community 1mo ago What does "Safe AI" look like? [D] ​ For open-weight LLMs, how practical is it to study defenses against post-release fine-tuning that weakens refusal or safety behavior? I've been seeing “uncensored” or “heretic” variants of new models appear very quickly after release, which raises a question I’m curious… 28 arXiv — Machine Learning research 1mo ago DeadPool: Resilient LLM Training with Hot-Swapping via Zero-Overhead Checkpoint arXiv:2607.01646v1 Announce Type: new Abstract: State-of-the-art large language model (LLM) training takes tens of thousands of graphics processing units (GPUs) for months and encounters failures across the software and hardware stack. Existing fault-tolerance mechanisms either… 16 arXiv — Machine Learning research 1mo ago SCAPE: Accurate and Efficient LLM Training with Extreme Sparse Communication arXiv:2607.01678v1 Announce Type: new Abstract: Communication increasingly dominates the cost of Large Language Model (LLM) pre-training, especially under data-parallel and sharded training schemes, where gradient synchronization and parameter reconstruction overhead increase… 12 arXiv — Machine Learning research 1mo ago Model Merging as Probabilistic Inference in Fine-Tuning Parameter Space arXiv:2607.01689v1 Announce Type: new Abstract: Model merging aims to combine existing single-task solutions into a multi-task solution without additional data-driven fine-tuning.~Most existing approaches achieve this using geometric properties of local solution spaces. However,… 24 arXiv — Machine Learning research 1mo ago EPnG: Adaptive Expert Prune-and-Grow for Parameter-Efficient MoE Fine-tuning arXiv:2607.01789v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models scale efficiently but remain costly to adapt due to redundant experts and uniform parameter allocation. Existing parameter-efficient fine-tuning (PEFT) methods such as LoRA ignore MoE routing… 20 arXiv — Machine Learning research 1mo ago Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis arXiv:2607.01918v1 Announce Type: new Abstract: We present Zeus, a unified tuning-free Time Series Foundation Model (TSFM) that delivers superior performance across diverse analysis tasks without any task-specific fine-tuning. Unlike prior studies that primarily focus on… 19 arXiv — Machine Learning research 1mo ago kNNGuard: Turning LLM Hidden Activations into a Training-Free Configurable Guardrail arXiv:2607.02072v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in domains requiring guardrails to detect unsafe, off-topic, or adversarial prompts. Existing guardrails predominately rely on fine-tuning to build classifiers, which often… 10 arXiv — Machine Learning research 1mo ago Probing Chemical Language Models: Effects of Pre-training and Fine-tuning arXiv:2607.02140v1 Announce Type: new Abstract: Chemical language models (CLMs) are trained with linearized representations such as SMILES, yet it remains unclear which chemically meaningful substructures they encode. To foster a better understanding of CLMs, we conduct a… 22 arXiv — NLP / Computation & Language research 1mo ago Bayesian Sparse Low-Rank Adaptation for Large Language Model Uncertainty Estimation arXiv:2607.02182v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit remarkable reasoning capabilities, but their task-specific fine-tuning is notoriously plagued by overconfidence, severely hindering trustworthy deployment. We propose Data-Adaptive Lower-Rank… 25 arXiv — NLP / Computation & Language research 1mo ago HERMES: A Multi-Granularity Labeling Substrate for Pre-training Data Mixtures arXiv:2607.02266v1 Announce Type: cross Abstract: Most data-mixing methods assume the corpus has already been partitioned into groups, and the choice of those groups determines what a mixer can express. Existing labels, including provenance, topic or format taxonomies, and flat… 12 arXiv — NLP / Computation & Language research 1mo ago BamiBERT: A New BERT-based Language Model for Vietnamese arXiv:2607.02259v1 Announce Type: new Abstract: In this paper, we introduce BamiBERT, a new BERT-based pre-trained language model for Vietnamese that addresses key limitations of PhoBERT -- the current de facto Vietnamese text encoder. Trained from scratch on a 129GB corpus of… 30 arXiv — NLP / Computation & Language research 1mo ago Less Data, More Security: Advancing Cybersecurity LLMs Specialization via Resource-Efficient Domain-Adaptive Continuous Pre-training with Minimal Tokens arXiv:2507.02964v2 Announce Type: replace Abstract: The increasing scale of AI workloads demands High-Performance Computing (HPC) infrastructure and training methodologies that are both scalable and sustainable. While Large Language Models (LLMs) demonstrate exceptional natural… 24 r/LocalLLaMA community 1mo ago openlumara, my manually coded super-token-efficient harness, now works across any UI that can connect to an openAI endpoint! koboldlite, openwebui, you name it. basically, openAI bridge. yay! this was a long time coming, but it's finally here! you can now basically supercharge whichever UI you're already using with the power of openlumara . click that link for more information about openlumara itself. TL;DR: super token efficient framework built from the ground up… 25 arXiv — Machine Learning research 1mo ago FRAME: Learning the Adaptation Domain with a Mixture of Fractional-Fourier Experts arXiv:2607.00162v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) reparameterizes weight updates in a fixed basis: low-rank adapters operate in the spatial domain, while a recent line of spectral methods operates in a fixed Fourier domain. We argue that the… 36 arXiv — Machine Learning research 1mo ago Loss Smoothing for Stable Adaptation Under Distribution Shift arXiv:2607.00634v1 Announce Type: new Abstract: In settings such as fine-tuning and reinforcement learning, neural networks are often adapted under distribution shift. Standard adaptation methods typically optimize the target objective directly, inducing an abrupt change from… 38 arXiv — Machine Learning research 1mo ago Local Motion Matters: A Deconstruct-Recompose Paradigm for Reinforcement Learning Pre-training from Videos arXiv:2607.00808v1 Announce Type: new Abstract: Pre-training on large-scale videos to improve reinforcement learning efficiency is promising yet remains challenging. Existing methods typically treat the agent as an indivisible entity, modeling motion patterns globally. Such… 8 arXiv — Machine Learning research 1mo ago From Pixels to Temporal Correlations: Learning Informative Representations for Reinforcement Learning Pre-training arXiv:2607.00811v1 Announce Type: new Abstract: Unsupervised pre-training on large-scale datasets has demonstrated significant potential for improving the sample efficiency and performance of Reinforcement Learning (RL). Given the large-scale action-free internet videos,… 13 Page 6 of 10 · 500 articles ← Newer Older →
