News / #training Tag Training 500 articles archived under #training · RSS Sign in to follow r/LocalLLaMA community 18h ago Discover AI: A Learning Guide - 51 hands-on labs for learning how AI actually works. This is an offline app with 51 hands-on labs for learning how AI actually works, from neurons to agents and more... **A bit of insight** - 51 labs in 6 groups, from the basics (what a neuron is, gradient descent) through attention, RAG, agents, fine-tuning, quantization, serving… 32 arXiv — Machine Learning research 2d ago Sample-Adaptive Zeroth-Order Post-Training arXiv:2610.12566v1 Announce Type: new Abstract: Zeroth-order (ZO) optimization offers a memory-efficient alternative to backpropagation for fine-tuning large language models. However, existing ZO methods typically sample training examples uniformly, overlooking their evolving… 22 arXiv — Machine Learning research 2d ago A Categorical Atlas of Parameter-Efficient Fine-Tuning: Cones, Orbits, and Gauges arXiv:2610.12728v1 Announce Type: new Abstract: Parameter-efficient fine-tuning has hundreds of methods, usually compared by parameter count and benchmark score. We compare them as maps. A pretrained checkpoint is a base point in weight space, and a weight-space method is a… 21 arXiv — Machine Learning research 2d ago Cross-Behavior Policy Optimization: Unified Learning from Offline Demonstrations and On- and Off-Policy Rollouts via a Shared Regression Target arXiv:2610.12830v1 Announce Type: new Abstract: Reasoning post-training draws on offline demonstrations and policy-generated rollouts, yet conventional supervised fine-tuning (SFT) followed by reinforcement learning (RL) separates their objectives and limits continued reuse of… 20 arXiv — Machine Learning research 2d ago Robust Langevin Sampling under State-Dependent Heavy-Tailed Noise with Applications to LLM Fine-Tuning arXiv:2610.12865v1 Announce Type: new Abstract: Existing convergence guarantees for non-log-concave sampling with stochastic gradient Langevin Monte Carlo (SG-LMC) typically assume globally Lipschitz gradients and uniformly bounded noise variance. Both assumptions can fail even… 17 arXiv — Machine Learning research 2d ago Personalized Federated Direct Preference Optimization via LoRA Adapters arXiv:2610.12885v1 Announce Type: new Abstract: Federated Direct Preference Optimization (FedDPO) enables privacy-preserving alignment of language models across distributed agents without centralizing sensitive preference data. However, existing FedDPO analysis assumes all… 24 arXiv — Machine Learning research 2d ago Continually Post-training Small Models via Synthetic Self-distillation arXiv:2610.13113v1 Announce Type: new Abstract: Continual post-training faces a dual challenge of acquisition and retention: models must learn new capabilities while preserving prior knowledge. While Self-Distillation Fine-Tuning (SDFT) mitigates this using self-supervision with… 25 arXiv — Machine Learning research 2d ago PDE-PFN: Prior-Data Fitted Neural PDE Solver arXiv:2610.13334v1 Announce Type: new Abstract: Motivated by the success of large language models (LLMs) with broad generalizability and robustness to noisy or unreliable pre-training data, we explore in-context prediction of PDE solution fields using approximate pre-training… 10 arXiv — NLP / Computation & Language research 2d ago What Should SFT Learn? Conditional Information Gain for Generalizable Post-Training arXiv:2610.12995v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) remains a widely used approach for post-training large language models, because fixed demonstrations can directly introduce desired reasoning strategies and behavioral patterns. Yet forcing a model to… 31 arXiv — NLP / Computation & Language research 2d ago MoPrO-SD: Modular Prompt Optimization for Stance Detection arXiv:2610.13386v1 Announce Type: new Abstract: Stance detection remains challenging for large language models (LLMs) due to implicit opinions, sarcasm, and context-dependent expressions of stance. Although automatic prompt optimization offers an alternative to fine-tuning… 38 arXiv — NLP / Computation & Language research 2d ago Enhancing Legal Reasoning in Pre-trained Language Models via Knowledge Graph-Guided Multi-Task Pre-training arXiv:2610.13722v1 Announce Type: new Abstract: Legal language understanding requires reasoning over semi-structured regulatory documents, yet standard pre-training objectives do not explicitly target this structural capability. Because current legal language models rely solely… 8 ElevenLabs official-blog 5d ago What is voice activity detection and how does it work? Voice activity detection (VAD) classifies audio frames as speech or silence. Learn how VAD works, how it differs from endpointing, and how to test it 15 arXiv — Machine Learning research 5d ago Freeze the Decoder, Heal the Encoder: Parameter-Efficient Adaptation for SVD-Based KV-Cache Compression arXiv:2610.10552v1 Announce Type: new Abstract: Comparing parameter-efficient fine-tuning recipes under a single, shared learning rate is a common but flawed practice: when the arms being compared have very different trainable-parameter counts, a shared rate can simultaneously… 17 arXiv — Machine Learning research 5d ago Nullify: Null-Space Activation Steering for Training-Free LLM Unlearning arXiv:2610.10655v1 Announce Type: new Abstract: Large Language Models (LLMs) inevitably internalize substantial amounts of sensitive or private information during pre-training, while LLM unlearning aims to selectively erase specific knowledge to prevent privacy leakage with… 30 arXiv — NLP / Computation & Language research 5d ago Diffu-LoRA: A Novel Low-Rank Adaptation for Personalized Diffusion Models arXiv:2610.10550v1 Announce Type: new Abstract: Personalizing text-to-image diffusion models from a few reference images requires preserving subject identity while following prompts that describe new contexts. Full-model fine-tuning is parameter-intensive, whereas low-rank… 22 arXiv — NLP / Computation & Language research 5d ago SFT-as-Context Mitigates Forgetting in Supervised Fine-Tuning arXiv:2610.11132v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) equips large language models (LLMs) with specialized capabilities, but often comes at the cost of forgetting the general capabilities of their parent models (i.e., the pretrained models before… 29 arXiv — NLP / Computation & Language research 5d ago The "10th Juror": Open-Set Standpoint Screening for Bureaucratic Bias Detection arXiv:2610.11136v1 Announce Type: new Abstract: Presupposing the boundaries of bias is itself a form of bias. We study closed-loop bias governance for Dutch government documents, where a system must detect biased language, ground decisions in legal and contextual evidence,… 38 arXiv — NLP / Computation & Language research 5d ago SciTBERT: A family of chronologically consistent language models for scientific and technological language processing arXiv:2610.12207v1 Announce Type: new Abstract: Pre-trained transformer models are increasingly being used to study scientific and technological progress. Encoders tuned to paper or patent text outperform general-purpose models on downstream classification, regression, and… 38 r/MachineLearning community 6d ago Have URMs and UTs been integrated into frontier models? Or did they disappear into the dustbin of forgotten papers? [D] UT-based small models, despite being trained from scratch on these tasks without internet-scale pre-training, consistently outperform most standard Transformer-based Large Language models (LLMs) by a significant margin. 👈 The Universal Transformer (UT) extends the standard… 38 r/LocalLLaMA community 6d ago Currently having high success with this little niche finetune i found sitting in the corner of huggingface Currently having high success with this little niche finetune i found sitting in the corner of huggingface If you want to try it out here is a smaller quantisation iq3_s works really well in my codebases. Original Model:… 34 r/LocalLLaMA community 6d ago OpenAI-compatible TTS endpoint using OmniVoice: 0.3s response time I want to share a TTS server with an OpenAI-compatible API that generates speech really fast (about 0.3 seconds for a sentence on an RTX 3080) and can clone a voice from a short reference clip. I’ve optimized the server so generation starts quickly, and it processes long text in… 16 r/LocalLLaMA community 6d ago jevman: AI decision models play Pac-Man The other week I posted about Jev vs. Kev compared and since then, OpenAI released the decisions endpoint, Cloudflare released Clef and many here asked about Laya as well. This time we compared six popular decision models by making them play Pac-Man: kev 1.13, Kev 4B, Clef, Clef… 10 arXiv — Machine Learning research 6d ago Removing Information Content Does Not Certify Tamper Resistance in Open-Weight Models arXiv:2610.09004v1 Announce Type: new Abstract: Does removing harmful information make open-weight models resistant to fine-tuning attacks? We show that mutual information at release alone cannot universally certify slow recovery. Function-preserving reparameterizations leave… 8 arXiv — Machine Learning research 6d ago MaRK: Markov-adapted Recurrent Kernels for Dynamic Operator Conditioning in State Space Models arXiv:2610.09092v1 Announce Type: new Abstract: State Space Models (SSMs) offer an efficient alternative to Transformers for sequence modeling, yet conditioning pre-trained SSMs for iterative generation typically operates outside the recurrent operator, through input injection… 21 arXiv — Machine Learning research 6d ago Are Parameter-Efficient Fine-tuning Methods Really Different? arXiv:2610.09122v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) offers many parameterizations, yet their methodological and functional differences remain unclear. We compare six methods in language and diffusion models to examine how their… 35 arXiv — Machine Learning research 6d ago A Framework for the Systematic Review of ML Assets in AI Registries arXiv:2610.09551v1 Announce Type: new Abstract: Background: Modern software systems increasingly rely on Machine Learning (ML) assets (i.e., pre-trained models, datasets, benchmarks) for building, evaluating, and integrating ML-based systems. However, current exploration,… 14 arXiv — NLP / Computation & Language research 6d ago The Persona Hierarchy Model: Understanding Contextual Generalization in Fine-Tuning LLMs arXiv:2610.09384v1 Announce Type: new Abstract: Language models are routinely fine-tuned under a fixed context, such as a generic system prompt, persona or domain-specific instruction, yet the learned behavior sometimes stays confined to that context and sometimes broadly… 38 arXiv — NLP / Computation & Language research 6d ago MIRROR: From Imitation to Internalization in LLM Personalization arXiv:2610.09795v1 Announce Type: new Abstract: The demand for personalized LLMs is shifting from style imitation toward content quality. We investigate whether self-distillation can bridge this gap in existing fine-tuning paradigm. To address this limitation, we introduce… 20 arXiv — NLP / Computation & Language research 6d ago A Deafening Silence: Catastrophic Forgetting Lives in the Output Embeddings of Tokens the Data Never Speaks arXiv:2610.09835v1 Announce Type: new Abstract: Continual pre-training and fine-tuning in Large Language Models (LLMs) inevitably induce catastrophic forgetting, typically mitigated by replay using often-inaccessible original data. In this data-free regime, we analyze where… 35 Hugging Face official-blog 7d ago One Model Family, Two Gold-Level Results: Fine-Tuning Nemotron for IOI and IMO Back to Articles a]:hidden"> One Model Family, Two Gold-Level Results: Fine-Tuning Nemotron for IOI and IMO Enterprise + Article Published October 7, 2026 Upvote 2 Aleksander aficek nvidia Igor Gitman igitman nvidia Sean Narenthiran SeanNaren nvidia Mehrzad Samadi mehrzads… 25 arXiv — Machine Learning research 7d ago DART-ES: Difficulty-Aware Reweighting and Targeted Replay for Fine-Tuning LLMs with Evolution Strategies arXiv:2610.06993v1 Announce Type: new Abstract: Evolution Strategies (ES) enable memory efficient full parameter fine-tuning of large language models (LLMs) using only forward computation. However, standard ES uniformly averages rewards across problems and compresses problem… 16 arXiv — Machine Learning research 7d ago Decoupling What from Where: How Should a Small GUI Grounding Model Receive the Action Type? arXiv:2610.07444v1 Announce Type: new Abstract: A GUI agent decides which action to take and where to take it; we ask how a small grounding model should receive the action type. Fine-tuning Qwen2-VL-2B with LoRA on Android in the Wild, we compare a flat baseline with five ways… 15 arXiv — Machine Learning research 7d ago Which and When to Admit: Gradient Admission for Data-Centric Small Language Model Finetuning arXiv:2610.07553v1 Announce Type: new Abstract: LoRA fine-tuning adapts small language models (SLMs) to heterogeneous instruction data within a low-rank update subspace, making it vulnerable to three structural problems: conflicting gradients that cancel, static data selection… 7 arXiv — NLP / Computation & Language research 7d ago Turnslide: Scalable Multi-Turn Data Synthesis by Walking a Finite-State Machine arXiv:2610.07070v1 Announce Type: new Abstract: Small language models are inexpensive to serve and can run on private infrastructure, but base models are often not good enough at multi-turn tool calling, and fine-tuning them needs per-API data that rarely exists. Existing… 25 arXiv — NLP / Computation & Language research 7d ago Dynamic Positional Attention Modulation for Parameter-Efficient Fine-Tuning of Large Language Models arXiv:2610.07848v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) has become a standard approach for adapting large language models to downstream tasks. However, most existing PEFT methods rely on uniform and static adaptations, without accounting for the… 29 arXiv — NLP / Computation & Language research 7d ago Beyond Refusal Patterns: Safe-Role Internalization for Robust and Generalizable LLM Safety Alignment arXiv:2610.07023v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved remarkable capabilities but remain vulnerable to jailbreak attacks that elicit harmful or unsafe outputs. Existing safety alignment approaches, including Supervised Fine-Tuning (SFT) and… 18 Vercel — AI dev-tools 7d ago OpenAI Decisions API now available on AI Gateway OpenAI's Decisions API is now available on AI Gateway through an OpenAI-compatible /v1/decisions endpoint. Use it with GPT-6 Luna Decisions or any other decision model on AI Gateway. Decision models answer typed questions about a shared input and return probabilities, choices,… 9 r/MachineLearning community 8d ago Embedding Every Font with Neural Networks makes some Nice Structures (including a flower) [P] I've been working on a font searching tool for about a year now, and my investigations have centered around pre-training neural networks to produce embeddings of each font. I usually then need to post-train the networks to adapt them to the task of font searching. However, the… 33 r/LocalLLaMA community 9d ago nokia-applied-research/AnyJev: Turn any LLM into a Jev-style decision model Haven't seen posted here. The research project matters because it proves you can pull trustworthy decisions straight out of an LLM's internal state in a single forward pass (no generation loop, no parsing, no fine-tuning) essentially turning a slow chatbot into a fast and cheap,… 19 r/LocalLLaMA community 9d ago Speakrail - a low-latency fully-local voice assistant that runs on a single RTX 4090 tl;dr: I created a fully-local open-source full-duplex voice agent that rivals GPT-Live on some benchmarks. It uses Voxtral Realtime with a turn-taking head, a microturn-finetuned Gemma 4 12B and Breeze TTS 2 under the hood. Go try it out: https://github.com/speakrail/speakrail… 21 r/LocalLLaMA community 9d ago Finetuned 1.5B Qwen to generate bash commands at gpt-4o level using 400k synthetic examples + Fully opensource finetune dataset Purely a hobby side project to see how far I can push a really small model, using (mostly) automated training pipelines Full synthetic data: https://huggingface.co/datasets/dirac-run/ec-training-data Models: https://huggingface.co/dirac-run/ec-1.5b-gguf and… 15 arXiv — Machine Learning research 9d ago Fisher-Guided Submodular Data Selection for Continual Pre-Training of Large Language Models arXiv:2610.02593v1 Announce Type: new Abstract: Data selection is already a central bottleneck in large-language-model training, where web-scale corpora are noisy and token budgets are finite. In continual pre-training (CPT), it becomes a forgetting-control problem: a poorly… 35 arXiv — NLP / Computation & Language research 9d ago Counterexample Generation via Per-Theorem Symbolic Verifiers: When Imitation Hurts and Reinforcement Repairs arXiv:2610.02444v1 Announce Type: new Abstract: Large language models often solve a theorem forward yet fail to disprove a closely related false one: a falsification gap that supervised fine-tuning does not close and can actively worsen. We frame counterexample generation as… 23 arXiv — NLP / Computation & Language research 9d ago ROUTEAUDIT: Interaction-Aware Identification for Budgeted Multi-Verifier Routing arXiv:2610.02808v1 Announce Type: cross Abstract: Adaptive multi-verifier systems are commonly compared through endpoint quality-cost gaps, even when the verifier catalog, availability, accounting, information filtration, or scorer changes with the policy. We formulate verifier… 7 r/LocalLLaMA community 10d ago LLM Inference Dashboard Working on a resource dashboard, rich logs, lightweight 64mb cap, all local, scales on network API endpoints via collector, supports multiple engines (llama, strata, custom cuda engines, unsloth, LMS) Not public yet, but curious if anyone would be interested? It’s better logging… 7 r/LocalLLaMA community 10d ago Update #4: Post training yandex/AliceAI-80B-A3B [instruct!] from scratch Last update for those following: https://www.reddit.com/r/LocalLLaMA/comments/1wvyc3e/update_3_post_training_yandexaliceai80ba3b/ Project in a sentence: An instruct finetune of ALiceAI-80B-A3B-Base capable of agentic work and conversation. I'm creating a shallow distill of qwen… 7 arXiv — Machine Learning research 11d ago Every Batch Is Its Own Validation Set: Leave-One-Out Gradient Matching for Online Data Selection in LLM Fine-Tuning arXiv:2610.00436v1 Announce Type: new Abstract: Online batch selection fine-tunes a language model on the most useful part of each candidate batch. Selectors that match the gradient of the candidate batch are attractive because they need no held-out data, yet they rarely beat… 9 arXiv — Machine Learning research 11d ago Make Sparse Rewards Count: Density-Aware Reward Aggregation for Multi-Reward RL arXiv:2610.00574v1 Announce Type: new Abstract: Multi-reward reinforcement learning trains large language models to satisfy multiple behavioral objectives simultaneously. Reward-wise normalization, as used in GDPO, preserves reward-specific relative information within rollout… 8 arXiv — Machine Learning research 11d ago From Task Mixtures to Specialized Experts arXiv:2610.00580v1 Announce Type: new Abstract: In collaborative foundation model fine-tuning, client data is rarely homogeneous. Instead, clients typically possess unknown mixtures of distinct data distributions, or tasks. Conventional federated learning primarily addresses… 26 arXiv — Machine Learning research 11d ago Analysis of Quantized and Efficiently Adapted Protein Language Models arXiv:2610.00665v1 Announce Type: new Abstract: Background: Protein language models (PLMs) are increasingly used for sequence generation and property prediction, but their size makes fine-tuning and deployment expensive. The effects of quantization and parameter efficient… 29 Page 1 of 10 · 500 articles Older →
