News / #training Tag Training 500 articles archived under #training · RSS Sign in to follow 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 arXiv — Machine Learning research 1mo ago Staleness-Learning Rate Scaling Laws for Asynchronous RLHF arXiv:2607.01083v1 Announce Type: new Abstract: High-throughput RLHF systems often decouple rollout generation from policy optimization, leading to the use of stale rollouts during learner updates. In this work, we study the effect of such staleness in asynchronous GRPO. We make… 23 Page 8 of 10 · 500 articles ← Newer Older →
