News / #training Tag Training 500 articles archived under #training · RSS Sign in to follow arXiv — Machine Learning research 29d ago Pre-training with Graph Transformers arXiv:2609.13844v1 Announce Type: new Abstract: This article investigates pre-training strategies for graph transformers in the biochemistry domain. By conducting comprehensive experiments, the study reveals that supervised pre-training using computed properties as labels… 9 arXiv — Machine Learning research 29d ago A Multi-Resolution Multi-Domain Pre-Training Framework for Universal Traffic Forecasting arXiv:2609.13878v1 Announce Type: new Abstract: Spatio-temporal traffic data are central to intelligent transportation systems, yet their heterogeneity poses significant challenges for large-scale modeling. Existing pre-trained models often rely on a homogeneous modeling… 24 arXiv — Machine Learning research 29d ago Retrieval-Guided Fine-Tuning as Noisy Estimation: Risk bounds and Architectural Analysis arXiv:2609.14485v1 Announce Type: new Abstract: Retrieval-Guided Fine-Tuning (RAG-FT) incorporates retrieved data directly into the training objective, but the statistical consequences of noisy retrieval during training remain theoretically undercharacterized. We study this… 23 arXiv — NLP / Computation & Language research 29d ago Token Merging for Multilingual Speech Recognition: A Systematic Study Across Model Scale and Fine-Tuning arXiv:2609.13151v1 Announce Type: new Abstract: Leading multilingual speech recognition models like Whisper transcribe diverse, low-resource languages without language-specific training but are computationally expensive to deploy. Token merging mitigates this inefficiency by… 38 arXiv — NLP / Computation & Language research 29d ago LayerRoute: Adaptive Layer-Skipping with LoRA-Preserved Quality for Efficient LLM Inference arXiv:2609.13682v1 Announce Type: new Abstract: We introduce LayerRoute, a parameter-efficient method for adaptive transformer layer-skipping that combines per-layer hard-gated routing (trained via a straight-through estimator) with joint LoRA fine-tuning. LayerRoute augments… 11 arXiv — NLP / Computation & Language research 29d ago Learning to Refer from Estimated Listener Gaze arXiv:2609.14207v1 Announce Type: new Abstract: We propose to finetune vision-language models to generate more pragmatically optimal referring expressions by transforming observations of incremental listener comprehension, in the form of gaze scanpaths, into learning signals.… 29 arXiv — NLP / Computation & Language research 29d ago Corpus Characterization and Inverse Constitutional Fine-Tuning for Style-Aware Radiology Reports arXiv:2609.14226v1 Announce Type: new Abstract: Automated radiology report generation has advanced rapidly in diagnostic accuracy, yet generated reports frequently diverge from the stylistic conventions of authentic radiologist writing in structure, diction, and uncertainty… 11 arXiv — NLP / Computation & Language research 29d ago Route, Don't Fix: Regime-Dependent Decoding Correction and a Trajectory-Gated Router for Reliable Clinical LLM Answer Selection arXiv:2609.14825v1 Announce Type: new Abstract: Large language models (LLMs) are often deemed unsafe for clinical question answering because of their tendency to hallucinate. Retrieval augmentation, fine-tuning, and external verifiers require new infrastructure that clinical… 32 Hacker News — AI on Front Page community 1mo ago EuroBirdPortal – Live bird movements across Europe Article URL: https://www.eurobirdportal.org/ebp/en/ Comments URL: https://news.ycombinator.com/item?id=49693610 Points: 201 # Comments: 61 10 arXiv — Machine Learning research 1mo ago Clustering-Based Balanced Sampling and Allocation with Data Parallelism for High-Performance Fine-Tuning arXiv:2609.12584v1 Announce Type: new Abstract: Instruction-tuning datasets for large language models (LLMs) are often large, redundant, and imbalanced, limiting efficient adaptation. Naive large-batch fine-tuning repeatedly includes overrepresented sample groups while weakly… 30 arXiv — Machine Learning research 1mo ago Where Decoder Cosine Similarity Fails for SAE Feature Flow Discovery arXiv:2609.12591v1 Announce Type: new Abstract: Foundation models are increasingly adapted through fine-tuning, model editing, and alignment procedures while retaining previously acquired capabilities. Understanding the internal computations that support these adaptations is… 25 arXiv — Machine Learning research 1mo ago Distortion of AI Alignment Revisited: RLHF is a Decent Utilitarian Aligner arXiv:2609.12651v1 Announce Type: new Abstract: While Reinforcement Learning from Human Feedback (RLHF) is the standard paradigm for aligning large language models with human preferences, its effectiveness in pluralistic settings has been called into question. Notably, recent… 33 arXiv — NLP / Computation & Language research 1mo ago HypoKG: Evidence-Disciplined Biomedical Hypothesis Generation Beyond Endpoint Knowledge arXiv:2609.12260v1 Announce Type: new Abstract: Large language models (LLMs) can generate biomedical hypotheses, but it remains unclear whether they truly reason from scientific evidence or simply produce convincing-sounding ideas. To study this, we combine three major… 37 arXiv — NLP / Computation & Language research 1mo ago All Entities are Not Created Equal: Examining the Long Tail for Ultra-Fine Entity Typing arXiv:2410.17355v4 Announce Type: replace Abstract: Due to their capacity to acquire world knowledge from large corpora, pre-trained language models (PLMs) are extensively used in ultra-fine entity typing tasks where the space of labels is extremely large. In this work, we… 38 arXiv — NLP / Computation & Language research 1mo ago ShadowPEFT: Shadow Network for Parameter-Efficient Fine-Tuning arXiv:2604.19254v2 Announce Type: replace Abstract: Popular low-rank parameter-efficient fine-tuning (PEFT) methods represent adaptation as separate updates to selected backbone weights, without maintaining an explicit task-specific state that is updated and reused across depth.… 35 Hugging Face Daily Papers research 1mo ago PLC-DPO: Posterior Label Correction in Noisy and Ambiguous Preference Optimization Abstract Posterior Label Correction DPO improves preference optimization by routing noisy pairwise labels into clean, flipped, or tied cases using calibrated policy-reference margins. Generated by thinkingmachines/Inkling-Small Direct Preference Optimization (DPO) simplifies… 24 r/LocalLLaMA community 1mo ago internlm/Intern-S2 · Hugging Face from internlm: We introduce Intern-S2-397B , our most capable multimodal foundation model for scientific intelligence and long-horizon agents. Intern-S2-397B scales along three critical dimensions: pre-training, reinforcement-learning task coverage, and interactive agent… 6 r/LocalLLaMA community 1mo ago I built a serverless hosting platform for LoRA adapters with vLLM It’s always bothered me that after fine-tuning a model for a project, there isn’t a particularly easy way to host it without either running it locally and keeping a GPU on 24/7 or paying for an entire GPU server. There are managed options for LoRA serving on top of vLLM (AWS),… 32 Simon Willison community 1mo ago So you want to use OpenRouter? So you want to use OpenRouter? One of OpenRouter's selling points is that it "handles fallbacks automatically and picks the most cost-effective option for each request", so you can call a single API endpoint for a model and get routed to the best available backend provider.… 33 r/LocalLLaMA community 1mo ago Is a ZIMA Board 2 + RTX 2000 ADA the cheapest path to a decent Qwen-3.8 27b self-contained endpoint? I just watched a YouTube from Luke’s Dev Lab where he literally just plugged a RTX 2000 ADA Into the side of the Zima Board 2’s PCIE socket and it just friggin worked and had great token speed despite running on shitty Ollama. Ran off the Zima’s power supply and everything.… 26 r/LocalLLaMA community 1mo ago CodeFinetuner: Fine-tune a local code autocomplete model on your own codebase Hi everyone, I was interested in learning LoRA fine-tuning, and ended up building CodeFinetuner over the past few months, a full pipeline that fine-tunes a small code autocomplete model (e.g. Qwen2.5-Coder-3B) specific to a codebase. You can then use the resulting GGUF model via… 33 Hugging Face Daily Papers research 1mo ago Building Multilingual Bridges: Data Mixing as the Pillar of Generalization for In-Language Reasoning Abstract Optimized supervised fine-tuning data composition enables reasoning models to consistently process and respond in diverse non-English languages without requiring reasoning supervision in each target language. Generated by thinkingmachines/Inkling-Small Reasoning… 36 r/LocalLLaMA community 1mo ago Fine-tuning Qwen 3 4B Base on 100 zebra puzzles yielded +31% on MATH-500. 6.5-min (Single H100/H200) reproduction notebook included.   submitted by   /u/TGSCrust [link]   [comments] 36 arXiv — Machine Learning research 1mo ago Understanding LoRA Rank Trade-offs in Diffusion Model Fine-Tuning arXiv:2609.10656v1 Announce Type: cross Abstract: Selecting LoRA rank for diffusion fine-tuning requires balancing quality and compute cost. We present a controlled study on CIFAR-10 using a DDPM U-Net with ranks {2,4,8,16,32}, fixed optimization settings, and a reproducible… 6 Hugging Face Daily Papers research 1mo ago Train Smarter, Not Harder: Switching Signal-Guided Training in Active Learning Abstract HybridAL adaptively switches from retraining to fine-tuning during active learning based on online stabilization signals, reducing training time while preserving accuracy and improving calibration. Generated by thinkingmachines/Inkling-Small Training strategy, namely… 6 arXiv — Machine Learning research 1mo ago Newton Matching for Generative Modeling: A Unified Framework for Fine-Tuning and Sampling arXiv:2609.05727v1 Announce Type: new Abstract: We develop Newton Matching, a unified framework for fine-tuning and sampling in generative modeling. The target is $\pi\propto\mu e^{\tau r}$, where $r$ is the reward, $\tau>0$ the inverse temperature, and $\mu$ denotes the… 16 arXiv — Machine Learning research 1mo ago RAPTOR: Role-Aware Private Training for Mixture-of-Experts arXiv:2609.05770v1 Announce Type: new Abstract: Differentially private (DP) fine-tuning methods treat sparse Mixture-of-Experts (MoE) models as a single dense block, ignoring that shared layers see all data while experts only see routed records. We identify and formally… 20 arXiv — Machine Learning research 1mo ago One Rate Is Not Enough: Adaptive Anisotropic Learning Rates for LoRA Fine-Tuning arXiv:2609.05885v1 Announce Type: new Abstract: Low-rank adaptation (LoRA) has become the standard for parameter-efficient fine-tuning of large language models. Most LoRA variants follow a uniform-LR convention, applying a single global learning rate across every rank-one… 17 arXiv — Machine Learning research 1mo ago ACE: Adapter Consolidation across Experts for Parameter-Efficient Fine-Tuning of MoE LLMs arXiv:2609.06072v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) of mixture-of-experts (MoE) models commonly attaches a separate low-rank adapter to each expert. This expert-wise design fragments adaptation in three ways: capacity is split across narrow… 12 arXiv — Machine Learning research 1mo ago FedSubMuon: Communication-Efficient Federated LLM Fine-Tuning via Structured Subspace Muon arXiv:2609.06073v1 Announce Type: new Abstract: Federated fine-tuning adapts large language models (LLMs) to decentralized client data, but its scalability in cross-device training is often limited by the high communication cost. Muon is an optimizer that improves optimization… 18 arXiv — Machine Learning research 1mo ago Inducing Emergent Misalignment from Reward Hacks with Iterative DPO arXiv:2609.06649v1 Announce Type: new Abstract: Reward hacking during reinforcement learning from verifiable rewards (RLVR) can induce reward seeking and broad misalignment in language models. Studying this misgeneralization is important for developing better threat models and… 32 arXiv — Machine Learning research 1mo ago SwiftExplorer: Training-free Diffusion Model Alignment with Swift Diversity Exploration arXiv:2609.06651v1 Announce Type: new Abstract: Diffusion models have general generative abilities but struggle to align with specific objectives. Fine-tuning can improve alignment, yet its training cost is often prohibitive. This led to training-free methods that apply… 22 arXiv — Machine Learning research 1mo ago Data Efficient Sample Selection for In-Context Learning arXiv:2609.06670v1 Announce Type: new Abstract: The In-context learning (ICL) paradigm aids large language models (LLMs) to adapt to new tasks without need for fine-tuning. However, selecting an optimal combination of demonstration examples from a large pool of example subsets… 21 arXiv — NLP / Computation & Language research 1mo ago Osprey: Target-agnostic Pre-training Makes Stronger Drafters in Speculative Decoding arXiv:2609.09338v1 Announce Type: new Abstract: Speculative decoding is critical for accelerating LLM inference. However, the speedup is fragile: drafters are typically trained against a narrow distribution for a single target model, and their acceptance rate collapses under… 12 arXiv — NLP / Computation & Language research 1mo ago Edu-QuRating: Multi-Dimensional Educational Data Curation with Distilled Pairwise Judgements arXiv:2609.09425v1 Announce Type: new Abstract: Educational data filters have become a practical way to improve language-model pre-training, but most filters treat educational value as a single scalar property. This may be too broad for some applications, especially if the data… 32 arXiv — NLP / Computation & Language research 1mo ago Towards Stress-Aware Sentence-Level Filipino G2P With Weakly-Supervised ByT5 Fine-Tuning arXiv:2609.09974v1 Announce Type: new Abstract: Grapheme-to-phoneme conversion (G2P) refers to the task of converting a sequence of graphemes to a corresponding sequence of phonemes. While Filipino G2P is fairly straightforward due to its shallow orthography, the inclusion of… 19 arXiv — NLP / Computation & Language research 1mo ago SalamandraTA at WMT 2026 Terminology Shared Task: Hard Examples Are Better Teachers arXiv:2609.09999v1 Announce Type: new Abstract: Terminology-aware translation asks for more than a correct translation: the output must use the exact terms a glossary prescribes. The standard recipe, fine-tuning on glossary-annotated translation pairs, hides an inefficiency: for… 28 arXiv — NLP / Computation & Language research 1mo ago Active Adaptation, Not Static Defense: Temporal Dynamics of Preventative Steering in Adversarial Fine-Tuning arXiv:2609.10142v1 Announce Type: new Abstract: Large language models remain fragile against malicious fine-tuning, motivating training-time defenses against harmful persona drift. Preventative Steering injects undesirable-trait persona vectors during fine-tuning and removes… 36 arXiv — NLP / Computation & Language research 1mo ago From Retrieval to Weights: Parametric Individualization of Small Language Models with Individual Text Corpora arXiv:2609.10155v1 Announce Type: new Abstract: We approach a cognitive simulation perspective on episodic and semantic memory in multiple-choice question answering by incorporating text from individual text corpora (ITC) into retrieval-augmented generation and DoRA fine-tuning.… 18 arXiv — NLP / Computation & Language research 1mo ago Fine-Tuning a KV Cache Concatenation-Aware Model or Recomputing KV Caches? Why Not Both? arXiv:2609.09768v1 Announce Type: cross Abstract: In Retrieval-Augmented Generation (RAG) systems, a large number of retrieved chunks are concatenated to form the input context so that users can receive high-quality responses based on external knowledge. As a result, the input… 34 Hugging Face Daily Papers research 1mo ago Revisiting Complete Reasoning Traces for Post-Training Abstract Large language models gain reasoning improvements from truncated trajectory endpoints rather than full reasoning traces, reducing redundancy while benefiting supervised fine-tuning and reinforcement learning. Generated by thinkingmachines/Inkling-Small Large language… 5 r/LocalLLaMA community 1mo ago Mention if a "new model" is a finetune A few posts tagged with "new model" present models that are finetunes. My opinion : I'd rather have the "new model" tag reserved for new "major" releases, like a new Qwen model, Deepseek V4 -> Deepseek V4.1, etc., that involved a new pretrain or intensive post-training (in… 32 Hugging Face Daily Papers research 1mo ago Marigold V2: Revisiting Diffusion Transformers for Monocular Depth Estimation Abstract Marigold V2 repurposes diffusion transformers for monocular depth estimation via single-step flow-matching inference, semantic alignment, and a Sinkhorn-based two-stage fine-tuning protocol, yielding sharper out-of-distribution depth maps and strong results on related… 31 r/LocalLLaMA community 1mo ago Qwen/Qwen-Drive-1.0-4B · Hugging Face I don't think anyone posted about this here, but Qwen released a finetuned version of 3.5 4 for driving. The full Bf16 checkpoint is 9B. This is a very interesting development of Chinese AI labs tackle self driving next with open weight models.   submitted by  … 4 arXiv — NLP / Computation & Language research 1mo ago MedProb: Probing Internal Representations of Vision-Language Models for Medical Question Answering arXiv:2609.04336v1 Announce Type: new Abstract: Medical visual question answering (Med-VQA) is often assumed to require medical fine-tuning, large models, or complex multi-agent pipelines. We revisit this assumption with \textbf{MedProb}, a lightweight probing framework that… 10 arXiv — NLP / Computation & Language research 1mo ago Cultural Misalignment in Large Language Models: Detection, Measurement, and Mitigation Through Targeted Fine-Tuning arXiv:2609.04485v1 Announce Type: new Abstract: We evaluate three open-weight LLMs (Gemma3-12B from the USA, Bielik-11B-v3 from Poland, and Qwen3-4B from China) against World Values Survey Wave 7 data for 63 demographic personas across three countries, using normalized… 25 arXiv — NLP / Computation & Language research 1mo ago Towards Understanding Pause Token Fine-Tuning Dynamics: A Mode Retention Perspective arXiv:2609.04489v1 Announce Type: new Abstract: Pause-token methods improve LLM reasoning by inserting special tokens into sequences. Prior work explains these gains through computational expressivity. However, there is relatively little investigation into the training dynamics… 25 Hugging Face Daily Papers research 1mo ago Iris: Climbing to the Search Frontier Abstract Two large-scale search agents are trained via a multi-stage pipeline combining supervised fine-tuning and reinforcement learning against live search, achieving state-of-the-art open-source results on complex web benchmarks through rigorous trajectory filtering and… 15 r/LocalLLaMA community 1mo ago Using GPT Astra to teach Qwen Next how to sculpt in 3D in Blender. Instead of distillation, fine-tuning, etc., I found a faster approach: having Codex+Astra to teach OpenCode + MCP Blender to make the modifications. Goal: achieve the same result using specific skills and steps. Astra is phenomenal, just as phenomenal as it is at quickly burning… 34 Anthropic SDK (Python) releases dev-tools 1mo ago v1.4.0 1.4.0 (2026-09-04) Full Changelog: v1.3.0...v1.4.0 Features api: add Claude Tag category and user breakdowns to usage reports ( 9fce1e4 ) api: add named types for organization compliance settings state ( 1de1957 ) api: add support for sending a workspace ID on more endpoints (… 32 Page 4 of 10 · 500 articles ← Newer Older →
