News / #training Tag Training 500 articles archived under #training · RSS Sign in to follow arXiv — Machine Learning research 12d ago Uncertainty-Normalized Margins for Direct Preference Optimization arXiv:2609.38647v1 Announce Type: new Abstract: Direct preference optimization (DPO) models binary preferences through a Bradley-Terry model with a common noise scale, without explicitly accounting for preference strength or prompt-dependent uncertainty from human feedback. We… 14 arXiv — Machine Learning research 12d ago Understanding Off- vs On-Policy Distillation: A Tale of Distinct Training Objectives arXiv:2609.38666v1 Announce Type: new Abstract: On-policy distillation (OPD) learns from teacher feedback on student-generated responses and has shown promise in reducing forgetting relative to supervised fine-tuning (SFT). However, its benefits and fragility remain incompletely… 36 arXiv — Machine Learning research 12d ago Robust Risk-Sensitive Reinforcement Learning from Corrupted Human Feedback arXiv:2609.38938v1 Announce Type: new Abstract: Reinforcement learning with human feedback (RLHF) learns from human comparisons, which can be corrupted or deliberately manipulated. This paper studies online risk-sensitive RLHF with static conditional value-at-risk (CVaR) under… 23 r/LocalLLaMA community 13d ago DDR4/PCIe4 vs DDR5/PCIe5 for LLMs- I benchmarked them for pre-training. What are your thoughts? Hello everyone. I've recently ran some experiments comparing DDR4/PCIe4 and DDR5/PCIe5 for AI workstations on a pre-training run, and would like to hear yours thoughts. First of all, and as a summary of my results ( code here:… 8 Hacker News — AI on Front Page community 13d ago Clef: Open-source decision models, and new RL fine-tuning platform Article URL: https://blog.cloudflare.com/clef-decision-models/ Comments URL: https://news.ycombinator.com/item?id=49923692 Points: 246 # Comments: 92 18 NVIDIA Developer Blog official-blog 13d ago Fine-Tuning NVIDIA Nemotron for Saudi Arabic Dialects, with a Path to Other Languages Automatic speech recognition must handle how people actually speak, not only the languages and styles that dominate pretraining data. Regional dialects and... 35 arXiv — NLP / Computation & Language research 13d ago Zero-Compute Cross-Lingual Transferability Estimation Using Typological Feature Proxies arXiv:2609.39640v1 Announce Type: new Abstract: Cross-lingual transfer describes how knowledge in a source language benefits a target language. Measuring it quantitatively requires broad multilingual pre-training, as prior work has done with cross-lingual transfer matrices. We… 9 arXiv — NLP / Computation & Language research 13d ago Synthetic Pre-pretraining Survives Scale, but Not as a Grammatical Prior arXiv:2609.39827v1 Announce Type: new Abstract: Pre-pretraining (PPT) on synthetic non-natural language data improves token efficiency during language model pre-training (PT). Prior work attributes this gain to a grammatical prior, i.e., a structural inductive bias learned… 38 arXiv — NLP / Computation & Language research 13d ago LLM Persona Unlearning arXiv:2609.39882v1 Announce Type: new Abstract: Pre-training equips large language models (LLMs) with a broad repertoire of behavioral patterns associated with roles, styles, values, and goals. Post-training teaches conditional enactment and makes a helpful Assistant the… 33 Anthropic SDK (Python) releases dev-tools 13d ago v1.11.0 1.11.0 (2026-09-30) Full Changelog: v1.10.0...v1.11.0 Features api: add list spend limits endpoint ( e8f0cac ) Chores client: deprecate sonnet 4.5 ( 22b36b1 ) 7 r/LocalLLaMA community 14d ago i would like to learn deeply about fine-tuning local models before burning money There's so many new techniques like RL, RL LoRA, QLoRA, CPT LoRA. I believe i would have a usecase for them, but i don't know where to learn, youtube is filled with bad quality tutorials if i just search and the good channels (fireship, bycloud) don't cover these as they're… 24 arXiv — Machine Learning research 14d ago Reducing the Adaptation Gap Through Reachable Fisher Geometry arXiv:2609.36329v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) determines not only how many parameters are trained, but also which local directions a model can move in, so similar adapters can affect subgroup losses differently. Since curvature matrices… 26 arXiv — Machine Learning research 14d ago Theory on Attention Dynamics for Out-of-Distribution In-Context Learning arXiv:2609.36448v1 Announce Type: new Abstract: Transformers have demonstrated remarkable in-context learning (ICL) capabilities, enabling them to perform new tasks without additional fine-tuning. However, their performance often deteriorates when encountering… 13 arXiv — NLP / Computation & Language research 14d ago FD-VAD: Semantic Endpoint Detection for Streaming Full-Duplex Speech arXiv:2609.35791v1 Announce Type: new Abstract: Natural turn-taking in full-duplex voice interaction requires determining from partial speech whether a pause reflects hesitation or a completed conversational intent. Acoustic voice activity detection lacks this semantic… 30 arXiv — NLP / Computation & Language research 14d ago The Canonical Order Problem: When Large Language Models Are Unreliable Knowledge Bases for Multi-Valued Relations arXiv:2609.36209v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as knowledge bases (KBs) due to the vast amount of knowledge they acquire during pre-training. While many works focus on extracting single relational triples, most real-world… 9 arXiv — NLP / Computation & Language research 14d ago VLM Fine-Tuning for End-to-End Combinatorial Optimization arXiv:2609.37175v1 Announce Type: new Abstract: Large language models (LLMs) have provided a unified interface for end-to-end combinatorial optimization (CO), but textual serialization alone may obscure spatial and relational structures that are important for generating… 33 r/LocalLLaMA community 14d ago SFTMill: Easily [off-policy] distill any existing LLM with an OpenAI Compatible Endpoint. Turn any behavioral goal into a comprehensive dataset. Disclaimer: Any* means any model that exposes its CoT without it being censored. Hey guys - half a tutorial/guide, and half an I built this, so I went for resources. This is something that I created for myself recently when I couldn't find any good existing solution. I wrote… 21 r/LocalLLaMA community 15d ago 50B+ MoEs with few active parameters, what's the sweet spot for intelligence, agent speed, and affordable fine-tuning? I’m building a Polish General purpose legal Model that drafts documents, answers questions using legal sources, and has enough coding ability to handle some automation. The workflow is very tool-heavy: Question → many sequential tool calls → final answer/document Think Claude… 5 arXiv — Machine Learning research 15d ago Parameters vs. Context: TRACE Fine-Tuning for Robust Retrieval-Augmented Generation arXiv:2609.30337v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) mitigates knowledge obsolescence and factual hallucination in large language models by introducing external context. However, when retrieved knowledge conflicts with the model's internal… 11 arXiv — Machine Learning research 15d ago EPOC: Endpoint-Preserving Online Correction With Compressed Residual State for Multi-Horizon Time Series Forecasting arXiv:2609.30929v1 Announce Type: new Abstract: Completed multi-horizon forecasts provide residual feedback for a fixed forecaster, but retaining full residual blocks increases auxiliary state. We propose Endpoint-Preserving Online Correction (EPOC) with a compressed residual… 11 llama.cpp releases dev-tools 15d ago b11240 server : support typed content (vision/audio/video) input for /v1/embeddings endpoint ( #29556 ) server : support multimodal input for /v1/embeddings (Qwen3-VL-Embedding) Accept the OpenAI-style wrapped content array format for multimodal embedding requests. Each {"content":… 10 r/LocalLLaMA community 16d ago Searching for 3.8 35B: Qwen3.6-35B-A3B (Testing 5 Finetunes vs. Base) TL;DR -- You should probably just use base Qwen3.6-35B, as only Occamy-1.0 is competitive with it. Tiel is a major let-down, worse than Ornith. KAT surprises (good), Nex surprises (bad). This post is long. Sorry, lots to cover. I think we all want to see a next-generation small… 12 r/LocalLLaMA community 16d ago Swift 1.5 + HyperQwen = 37% less task completion time at 100+ tps w/ 150k context on RTX 3090 Hi everyone :) The amazing Swift finetunes of Qwen3.8 27B generate much fewer tokens at mostly similar benchmark performance to the original model, while repos like HyperQwen (formerly syv-ai/qwen38-27b-rtx3090) deliver insane TPS on an RTX 3090. To get the best of both worlds,… 15 r/LocalLLaMA community 16d ago ImaJev-4b: I spent 15 days fine-tuning a 4B model to make business decisions from text and photos, and it just ranked #1 of 91 on JevBench & ahead of GPT-5.6 Luna on DecisionBench Some context first. I am process improvement / business consultant and had worked with Fortune 500 companies on improving their processes around refunds, returns, customer support, etc. This entire thing had a lot of complex decision making and generally every decision /… 29 arXiv — NLP / Computation & Language research 16d ago A Survey on Fake Review Detection: From Pre-trained Language Models to Large Language Models arXiv:2609.30292v1 Announce Type: new Abstract: Online reviews shape consumer decisions, platform governance, and corporate reputation.Fake reviews compromise this information channel by injecting deceptive evidence into rating systems, recommendation pipelines, and public trust… 28 arXiv — NLP / Computation & Language research 16d ago Understanding the Role of Prompt Template in Knowledge Distillation for Safety Alignment arXiv:2609.30802v1 Announce Type: new Abstract: Prior research has demonstrated that the choice of prompt template during Supervised Fine-Tuning (SFT) significantly impacts the robustness of safety alignment afterwards. However, the influence of template selection during… 35 arXiv — NLP / Computation & Language research 16d ago Estimating and Orthogonalizing Unknown Pre-training Gradients for Continual Fine-tuning of Large Language Models arXiv:2609.30935v1 Announce Type: new Abstract: Continual fine-tuning is essential for large language models (LLMs) to dynamically adapt to real-world environments, yet it inevitably suffers from catastrophic forgetting, particularly the performance degradation of previous tasks… 19 arXiv — NLP / Computation & Language research 16d ago Muslim: A Deployed Arabic Voice AI Platform for Grounded Islamic Knowledge arXiv:2609.31511v1 Announce Type: new Abstract: We present Muslim, a production Arabic voice AI platform serving grounded, sourced Islamic knowledge to real users. Beyond a real-time voice pipeline (NeMo Arabic ASR, an OpenAI-compatible LLM endpoint, self-hosted TTS) and a… 9 arXiv — NLP / Computation & Language research 16d ago Symbiotic Architecture for Post-Hoc Audio Extension of Frozen Language Models arXiv:2609.30784v1 Announce Type: cross Abstract: This paper proposes an architecture for equipping large language models (LLMs) with audio-understanding capabilities without fine-tuning their weights. The proposed symbiotic architecture employs an injector module that writes… 23 r/LocalLLaMA community 17d ago Public MCP server for Canadian privacy law data (free, no auth) - works with any client that speaks Streamable HTTP I built this, so the disclosure goes up front. It's a free, public MCP server plus a REST API with Canadian privacy law data. The MCP endpoint is at https://movahedi.ca/mcp and it uses Streamable HTTP, so any client that speaks that transport can connect. No signup, no API key,… 36 r/LocalLLaMA community 19d ago Jev vs. Kev: open-source Jev alternative tested side by side We hosted Kev 4B (Jared Palmer's Apache-2.0 fine-tune of Qwen3.5-4B) and ran it side by side with Jev on the same endpoint to see how it compares. We built a fresh set of 362 items published after both models shipped (new arXiv papers, Stack Exchange questions, GitHub issues),… 9 arXiv — Machine Learning research 19d ago Automatic Rank Allocation for Low-Rank Adaptation in Large Language Models via lp Regularization arXiv:2609.28998v1 Announce Type: new Abstract: Low-rank adaptation (LoRA) has become a popular parameter-efficient fine-tuning method for large language models. A key challenge in LoRA is how to determine the rank of each adaptation matrix, as rank directly controls its… 29 arXiv — NLP / Computation & Language research 19d ago Technical Manual for Toolkit for Confidence-Corpus Consistency via Fine-Tuning on a Fabricated Corpus arXiv:2609.28747v1 Announce Type: new Abstract: A language model's confidence in an answer is often read as a proxy for how well it knows the corresponding fact. This manual documents an open toolkit built to test that reading directly: a small causal language model is… 27 arXiv — NLP / Computation & Language research 19d ago Adaptive Fisher-Whitened Cross-Covariance for Low-Resource Speech Recognition arXiv:2609.29800v1 Announce Type: new Abstract: Adapting multilingual speech foundation models to low-resource languages remains difficult, especially for languages that are poorly represented during pre-training. While parameter-efficient fine-tuning (PEFT) reduces the cost of… 29 arXiv — NLP / Computation & Language research 19d ago The Fellowship of the Query: Learning Retrieval Actions arXiv:2609.28653v1 Announce Type: cross Abstract: Retrieval-augmented question answering requires control decisions about when to decompose a question, search, reformulate, extract evidence, synthesize facts, verify progress, and stop. We study whether trajectory fine-tuning can… 16 r/LocalLLaMA community 20d ago FreedomIntelligence/HuatuoGPT-3-27B · Hugging Face from FreedomIntelligence: HuatuoGPT-3-27B is a medical LLM built on Qwen3.8-27B with One-stage Policy Optimization (OnePO) . OnePO adapts language models to medicine in a single reinforcement-learning stage, without preceding domain-specific supervised fine-tuning. Teacher… 12 arXiv — Machine Learning research 20d ago WTF?! Simulation-Free Reinforcement Learning with Wasserstein-Tilted Flow Maps arXiv:2609.27033v1 Announce Type: new Abstract: Reward fine-tuning aims to update a pre-trained flow-based generative model to improve the downstream reward of its generated samples. Existing methods typically formulate this problem as sampling from a reward-tilted distribution,… 27 arXiv — Machine Learning research 20d ago Repurposing Pre-trained LLMs as High Fidelity Continuous Text Autoencoders arXiv:2609.27248v1 Announce Type: new Abstract: Next-token prediction has enabled highly fluent autoregressive language models, but it represents global structure only indirectly through sequential factorization. In contrast, high-fidelity autoencoders have become a standard… 22 arXiv — NLP / Computation & Language research 20d ago Does Step Law Transfer to Small-Scale Language Models? An Empirical Recalibration Below 59M Parameters arXiv:2609.27581v1 Announce Type: cross Abstract: Step Law gives power-law formulas for the optimal peak learning rate eta* and batch size B* when pre-training language models. It was calibrated on models between 59M and 1B parameters; the small-model regime N < 59M was never… 31 arXiv — NLP / Computation & Language research 20d ago Can One Adapted Model Do It All? Fine-Tuning Strategy Selection for Customer Support LLMs arXiv:2609.27262v1 Announce Type: new Abstract: Production customer-support systems often require LLMs to support multiple skills, such as intent classification, question answering, summarization, or tool-use decisions. A central deployment question is whether these skills… 15 arXiv — NLP / Computation & Language research 20d ago Six Layers Less: Encoder Pruning for Whisper with Label-Free Recovery arXiv:2609.27980v1 Announce Type: new Abstract: Pruning large pre-trained transformer-based ASR models such as OpenAI's Whisper has seen great adoption, as pruning the decoder led to significant end-to-end transcription speedups. For instance, the {\tt whisper-large-v3-turbo}… 32 arXiv — NLP / Computation & Language research 20d ago Fine-Tuning LLMs for Translation: General Forgetting Mitigation Does Not Preserve MT-Specific Instruction Following arXiv:2609.28395v1 Announce Type: new Abstract: Fine-tuning large language models on parallel data improves translation quality but can cause catastrophic forgetting. Mitigation methods are generally evaluated by retention on general benchmarks. We ask whether these findings… 34 arXiv — NLP / Computation & Language research 20d ago Contrastive Learning for Authorship Verification arXiv:2609.28471v1 Announce Type: new Abstract: Our results show that contrastive learning outperforms a classification-based approach to authorship verification under the tested settings. We identify loss function, batch size, training duration, pre-trained model, input context… 9 arXiv — NLP / Computation & Language research 20d ago Evaluation of pre-trained models for pedagogical assessment of novel AI-assisted educational questions arXiv:2609.27749v1 Announce Type: cross Abstract: The surge in AI-assisted generation of educational materials has outpaced our capacity to validate their pedagogical quality. Automated evaluation using Bloom Classifier models is a promising approach to assess educational… 13 arXiv — Machine Learning research 21d ago PermuFormer: Multi-Task Pretraining for Permutation Representation in Algebraic Combinatorics arXiv:2609.25438v1 Announce Type: new Abstract: Diverse pretraining has been shown to be an effective method for learning reusable, domain-aware representations that provide a starting point for fine-tuning on downstream tasks. While much of the excitement in AI for math has… 6 arXiv — Machine Learning research 21d ago From Experts to Sub-experts: Fine-grained Parameter-Efficient Fine-Tuning for MoE LLMs arXiv:2609.25655v1 Announce Type: new Abstract: As large language models (LLMs) scale rapidly, dense full-parameter adaptation becomes increasingly expensive, motivating sparse and modular architectures such as Mixture-of-Experts (MoE) models. This shift raises a key question… 22 arXiv — Machine Learning research 21d ago Signed Graph Pre-Training and Prompt Learning arXiv:2609.25722v1 Announce Type: new Abstract: Signed graphs arise in trust--distrust networks, financial correlation systems, biological interaction graphs, and many other domains in which edges can be positive or negative and may also be directed. While signed graph neural… 16 arXiv — Machine Learning research 21d ago Block-Level Weight-Space Structure Persists Under Post-Training: An Empirical Study Across LLM Families arXiv:2609.26147v1 Announce Type: new Abstract: Modern LLMs are deployed as families of post-trained variants (base, instruct, chat, code) derived from a shared set of pre-trained weights. We present an empirical study of how post-training transforms weight-space geometry,… 29 arXiv — NLP / Computation & Language research 21d ago ChainDoRA: Tensor-Train Factorized Weight-Decomposed Low-Rank Adaptation for Parameter-Efficient LLM Fine-Tuning arXiv:2609.25058v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) adapts large language models (LLMs) to downstream tasks while updating only a small fraction of their pretrained parameters. Low-Rank Adaptation (LoRA) uses two trainable low-rank matrices,… 10 arXiv — NLP / Computation & Language research 21d ago TransBERT: A Framework for Synthetic Translation in Domain-Specific Language Modeling arXiv:2609.26347v1 Announce Type: new Abstract: The scarcity of non-English language data in specialized domains significantly limits the development of effective Natural Language Processing (NLP) tools. We present TransBERT, a novel framework for pre-training language models… 26 Page 2 of 10 · 500 articles ← Newer Older →
