News / #training Tag Training 500 articles archived under #training · RSS Sign in to follow arXiv — NLP / Computation & Language research 1mo ago Capacity-Dependent Effects of Data Selection for Reasoning arXiv:2608.13721v1 Announce Type: cross Abstract: In reasoning supervised fine-tuning, candidate responses for the same instruction can differ substantially in how well they match the student's current distribution. Recent likelihood-based response selection methods suggest that… 9 arXiv — Machine Learning research 1mo ago Multi-Objective Bayesian Optimization for Model Merging arXiv:2608.14264v1 Announce Type: new Abstract: Model merging combines trained models directly in weight space, offering a compute-efficient alternative to additional fine-tuning. Selecting merge parameters is nevertheless difficult because downstream evaluations are expensive,… 9 arXiv — Machine Learning research 1mo ago Approximate Muon with low-rank adapters arXiv:2608.14492v1 Announce Type: new Abstract: The Muon optimizer shows clear benefits versus alternatives when pretraining neural networks. However, it is used less frequently for parameter-efficient fine-tuning (PEFT). One potential reason is that the most common PEFT method,… 18 arXiv — NLP / Computation & Language research 1mo ago When Gradient Importance Lies: Adaptive LoRA Rank Allocation Fails Under GRPO arXiv:2605.07366v2 Announce Type: replace Abstract: Adaptive rank allocation for LoRA - allocating more parameters to important layers and fewer to unimportant ones - consistently improves efficiency under supervised fine-tuning (SFT). We test whether this success transfers to… 34 Simon Willison community 2mo ago CORS Chat Tool: CORS Chat I built this today ( with GPT-5.6-Sol xhigh ) to help test Qwen 3.8 27B running in LM Studio on both my M5 MacBook Pro and an NVIDIA DGX Spark. It provides a web UI for exercising an OpenAI-Responses-compatible chat endpoint. I've tried it against LM Studio with… 36 r/LocalLLaMA community 2mo ago Building an open-source control plane for self-hosted vLLM, what would you want in it? Every time I self-host a model I rebuild the same stuff: start the container, set up a route, check why it died overnight, remember to shut the GPU off before it burns money. So I'm building a panel that handles it. Start/stop models, OpenAI-compatible endpoint, health checks… 25 Hugging Face Daily Papers research 2mo ago Intern-S2-Preview: Scientific Agentic Foundation Model Abstract Intern-S2-Preview is a scientific agentic foundation model series that integrates multimodal pre-training, multi-task reinforcement learning, and memory-augmented extensions to support long-horizon scientific reasoning and forecasting. Generated by… 33 arXiv — NLP / Computation & Language research 2mo ago I-SDPO: Instance-Level Adaptive Self-Distillation Policy Optimization arXiv:2608.12957v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) learns from reward differences within a rollout group, but receives no useful relative signal when every sampled response is incorrect. Privileged self-distillation can fill this gap with… 33 arXiv — Machine Learning research 2mo ago Into the ORBIT for Time Series: Training Regimes for Foundation Models arXiv:2608.13262v1 Announce Type: new Abstract: Time series foundation models (TSFMs) have advanced primarily through architectural innovation, while training regimes for large-scale heterogeneous corpora remain under-explored. As a result, pre-training distributions are often… 11 arXiv — Machine Learning research 2mo ago Intervention-Aware Clinical World Model for Post-Op Outcome Forecasting in Cardiology arXiv:2608.13518v1 Announce Type: new Abstract: Many clinical prediction models treat post-intervention outcomes as a one-step mapping from baseline measurements to a future endpoint. However, recovery after a procedure often unfolds as an irregular trajectory: clinical… 17 arXiv — NLP / Computation & Language research 2mo ago Comparative Analysis of Multilingual Pre-trained Models for Nepali Automatic Speech Recognition arXiv:2608.12327v1 Announce Type: new Abstract: Multilingual pretrained models nominally support Nepali, yet no controlled benchmark has compared them under a single fine-tuning protocol. We fine-tune six pretrained models (XLSR-53, IndicWav2Vec, MMS-1B, Whisper-Medium,… 34 arXiv — NLP / Computation & Language research 2mo ago Can Spectral-Clipping Enable Better Learning While Forgetting Less for Low-Rank Adaptation? arXiv:2608.12332v1 Announce Type: new Abstract: In recent years, low-rank adaptation (LoRA) has emerged as a significant paradigm that freezes pre-trained weights and introduces small, learnable adapters instead of fine-tuning the full set of parameters. In this work, we uncover… 24 arXiv — NLP / Computation & Language research 2mo ago LoRA-Diffusion: Parameter-Efficient Fine-Tuning via Low-Rank Trajectory Decomposition arXiv:2608.12328v1 Announce Type: new Abstract: Parameter-efficient fine-tuning methods such as LoRA have transformed the adaptation of large autoregressive language models, enabling task-specific customization with substantially fewer trainable parameters. However, these… 11 arXiv — NLP / Computation & Language research 2mo ago Reliability-Aware Sexism Detection: Combining DPO with Annotator Agreement and Token-Level Confidence Scoring arXiv:2608.12330v1 Announce Type: new Abstract: The detection of online sexism remains an open problem. Sexism detection is inherently subjective, yet most existing systems reduce multi-annotator labels to a single majority decision and treat all instances uniformly. This… 6 arXiv — NLP / Computation & Language research 2mo ago Mixture of Training: Recombining Small-Scale Scaffolded Pretraining Runs into a Larger Language Model arXiv:2608.13277v1 Announce Type: new Abstract: We ask whether language-model pre-training can be decomposed into smaller, independently trainable jobs that can later be recomposed into a coherent larger model. We introduce Mixture of Training (MoT), a scaffolded modular… 36 OpenAI official-blog 2mo ago How Fyxer built an AI executive assistant people trust Fyxer uses OpenAI models, fine-tuning, memory, and real user feedback to organize inboxes and draft emails in each user’s voice. 28 arXiv — NLP / Computation & Language research 2mo ago Weightless Fine-Tuning: Personalizing LLMs via Logit-Space Transport arXiv:2608.11342v1 Announce Type: cross Abstract: Supervised fine-tuning (SFT) is a standard approach for adapting LLMs to a target distribution, but in settings such as personalization, where each author requires separate weight access, optimization, storage, and retraining,… 35 arXiv — Machine Learning research 2mo ago Distillation of Foundation Models for Time-dependent PDEs arXiv:2608.11937v1 Announce Type: new Abstract: Foundation models for time-dependent partial differential equations (PDEs) are trained on large and diverse collections of physical systems and can generalize effectively to new downstream tasks. After fine-tuning on only a few… 25 arXiv — NLP / Computation & Language research 2mo ago Reinforcing Step-level Reasoning for Effective Self-Correction in LLMs arXiv:2608.11573v1 Announce Type: new Abstract: Achieving effective self-correction, where models verify and correct their own mistakes, remains a fundamental challenge for large language models (LLMs). In this work, we propose Self-Fix Step-DPO (SFS-DPO), a reinforcement… 35 arXiv — NLP / Computation & Language research 2mo ago AWARe: Mitigating Catastrophic Forgetting via Activation-Weighted Adaptive REtention arXiv:2608.11758v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) exhibit strong generalization and reasoning abilities due to large-scale multimodal pre-training. However, fine-tuning these models on downstream tasks often leads to catastrophic… 34 arXiv — NLP / Computation & Language research 2mo ago TELLME: Test-Enhanced Learning for Language Model Enrichment arXiv:2608.11788v1 Announce Type: new Abstract: Continual pre-training (CPT) has been widely adopted as a method for domain adaptation in large language models. However, CPT has consistently been accompanied by challenges, such as the difficulty of acquiring large-scale… 16 arXiv — NLP / Computation & Language research 2mo ago Benchmarking Trustworthiness of SLMs: Pre-trained vs. Compressed arXiv:2608.11981v1 Announce Type: new Abstract: Small Language Models (SLMs) have emerged as a more efficient alternative to traditional Large Language Models (LLMs), offering promising potential in resource-constrained scenarios. Existing approaches to building SLMs typically… 34 arXiv — NLP / Computation & Language research 2mo ago DexterSQL: Deep Schema Exploration and Rule-based Correction for Text-to-SQL Generation arXiv:2608.11889v1 Announce Type: cross Abstract: Prompting-based (\textit{i}.\textit{e}., non-fine-tuning) Text-to-SQL methods, where underlying large language model parameters are not changed for the task, face three problems: (\textit{i})~relying on coarse-grained schema… 38 Hugging Face Daily Papers research 2mo ago MBA: Multimodal Benchmark and Agents for Real-World Business Ideation Abstract Researchers introduce MBA-Bench, a multimodal benchmark for business ideation agents, and propose MBA-b and MBA-k models trained with creativity and feasibility rewards via LoRA fine-tuning and group relative policy optimization, significantly outperforming text-only… 21 r/LocalLLaMA community 2mo ago CohereLabs/North-Micro-Vision-Instruct · Hugging Face North Micro Vision Instruct is a 2.4B-parameter open-weight vision-language model with native-resolution image support, released under the Apache 2.0 license. It is designed as a compact foundation for prototyping, task-specific fine-tuning, and specialized multimodal… 8 arXiv — Machine Learning research 2mo ago Finding the Signal in the Spam: Jointly Learning Rewards and Worker Reliability from Pairwise Comparisons arXiv:2608.10045v1 Announce Type: new Abstract: The problem of learning from pairwise comparisons has been widely studied across many domains such as recommendation systems, social choice, and more recently, fine-tuning large language models. In this problem, the goal is to… 7 arXiv — NLP / Computation & Language research 2mo ago Procedural Fairness Failures in RLHF from Preference Averaging arXiv:2608.10126v1 Announce Type: cross Abstract: Reinforcement Learning from Human Feedback (RLHF) aggregates heterogeneous preferences into a single reward model, assuming preference homogeneity. When preferences are heterogeneous, this aggregation induces a procedural… 13 arXiv — Machine Learning research 2mo ago SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks arXiv:2608.10144v1 Announce Type: new Abstract: We consider federated parameter efficient fine-tuning of large neural networks with low-rank adaptation (LoRA,~Hu et al.\ 2022). Combining LoRA with federated PEFT introduces challenges absent from either setting alone: clients may… 22 arXiv — Machine Learning research 2mo ago Critic-Free Pretraining for Efficient Online Reinforcement Learning Fine-Tuning arXiv:2608.10473v1 Announce Type: new Abstract: Offline-to-online (O2O) reinforcement learning aims to leverage policies pretrained on static datasets while improving them through online interaction. However, directly reusing an offline-trained critic can hinder online… 19 arXiv — Machine Learning research 2mo ago Diffract: Spectral View of LLM Domain Adaptation arXiv:2608.10850v1 Announce Type: new Abstract: We study continual pre-training (CPT) as a mechanism for adapting general-purpose large language models to specialized domains: mathematics, instruction, code, and natural text. Using singular value decomposition of weight… 6 arXiv — NLP / Computation & Language research 2mo ago ReRound: Reconstructive Rounding to Resolve Midpoint Ambiguity in Calibration-Free LLM Quantization arXiv:2608.11045v1 Announce Type: cross Abstract: ReRound (Reconstructive Rounding) is a post-training quantization method that addresses the midpoint ambiguity inherent in standard round-to-nearest (RTN) schemes when quantizing weights near the centers of quantization… 30 arXiv — NLP / Computation & Language research 2mo ago Locally Deployable Small Language Models for Emergency Department Decision Support: A Systematic Benchmark of Fine-Tuning Strategies arXiv:2608.10273v1 Announce Type: new Abstract: Deploying large language models (LLMs) for decision support in emergency departments (EDs) faces two major challenges: privacy risks of transmitting patient data to closed-source commercial LLMs and the lack of systematic… 4 arXiv — NLP / Computation & Language research 2mo ago Reference-Free Post-Training of Open Large Language Models for Multilingual Machine Translation arXiv:2608.10812v1 Announce Type: new Abstract: We study reference-free post-training for multilingual machine translation with open large language models. Starting from the supervised-finetuned MiLMMT-46-v0.1 models, we apply Group Relative Policy Optimization (GRPO) with a… 30 arXiv — NLP / Computation & Language research 2mo ago REAP: Relation-Aware Elicitation and Parsing for Closed-Book Knowledge Base Construction from LLMs arXiv:2608.10963v1 Announce Type: new Abstract: We present the REAP system for the AKBC Shared Task 2026 on constructing knowledge bases from language models in a closed-book setting, subject to a budget of at most 32B parameters and no model fine-tuning. Our system combines… 14 arXiv — NLP / Computation & Language research 2mo ago Data Attribution of Emergent Misalignment with Persona Features arXiv:2608.11025v1 Announce Type: new Abstract: Emergent misalignment (EM) is the phenomenon where fine-tuning a language model on a narrow task leads to harmful behavior in unrelated domains. A leading mechanistic account attributes EM to persona features: latent directions… 29 arXiv — NLP / Computation & Language research 2mo ago myMediWhisper: Construction of Burmese Medical Speech Corpus and Whisper Fine-Tuning for Clinical Dialogue ASR arXiv:2608.11036v1 Announce Type: new Abstract: Although Whisper models benefit from large-scale multilingual pre-training, their performance on Burmese medical speech remains limited. This work presents a Burmese medical speech recognition framework built on a high-quality… 14 r/MachineLearning community 2mo ago Context-Induced Activation Drift: Long benign context passively decouples RLHF alignment without adversarial prompts (Mechanistic Interpretability + Ablation) [D] TL;DR: We observed that feeding a long, benign, thematically coherent context prefix ($L \in [100, 3000]$ tokens) into google/gemma-3-1b-it causes a massive passive shift in internal activations ($\Delta h_2 \approx 3434$) at deep layers ($\sim 85%$ depth). This leads to a logit… 7 r/MachineLearning community 2mo ago Research direction: Intelligent Model Weight transfer between LLMs [R] Few days ago I feel like I need to get started with researching about LLMs. One thing which strikes the most in my mind , how we can reduce the time required for pre-training an LLM model to just few minutes. Right now the most efficient method that we have is knowledge… 38 r/LocalLLaMA community 2mo ago Local Benchmark : Muse Glimmer 30B vs Qwen 3.6 27B vs Gemma4 31B (and many other models and finetunes) Needs a lot of requests compared to Qwen (almost twice) and Gemma (almost x3). Final score is fine, even though it is "not a coding model" https://wonderrico.github.io/local_llm_benchmark/benchmark-main.html more details on… 31 Hugging Face Daily Papers research 2mo ago Omega-S: A Functional Resilience Index for LLM Fine-Tuning Abstract Omega-S is a lightweight, data-free regularization penalty for low-rank fine-tuning that improves retention of original model capabilities by penalizing variance in weight-matrix node degrees. Generated by thinkingmachines/Inkling-Small Fine-tuning a large language… 36 Hugging Face Daily Papers research 2mo ago What to Edit Next: Visually Aligned Image-Editing Follow-Up Suggestions in Conversational Systems Abstract A three-stage multimodal framework improves follow-up edit recommendations in image-creation conversations by combining supervised fine-tuning, multi-objective reinforcement learning, and visual verification. Generated by thinkingmachines/Inkling-Small Conversational… 22 arXiv — Machine Learning research 2mo ago Router Sensitivity Under Lightweight Fine-Tuning Identifies Prunable Experts in Mixture-of-Experts Models arXiv:2608.07890v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models decouple total parameters from per-token compute, but deployment still requires storing every expert. Recent theory shows that pruning experts with the smallest router-norm changes during fine-tuning… 28 arXiv — Machine Learning research 2mo ago Spectral Outliers Reveal Dominant Learned Structure in Transformer Attention arXiv:2608.07921v1 Announce Type: new Abstract: We apply Marchenko-Pastur (MP) random matrix theory to pre-trained attention weights in order to separate each projection matrix into a random-like bulk and a set of spectral outliers. We validate this decomposition causally:… 20 arXiv — Machine Learning research 2mo ago ZeroLock: Concurrent Memory-Efficient LLM Training via Modular Update Decoupling arXiv:2608.07974v1 Announce Type: new Abstract: Large language model (LLM) fine-tuning at the edge adapts the model to scenario-specific data while preserving privacy. Although existing studies proposed pipeline parallelism to address the limited memory and computing resources… 10 arXiv — Machine Learning research 2mo ago Control-Diverse Reinforcement Fine-Tuning: Decoupling the Shared Control Bottleneck of RL Post-Training arXiv:2608.08224v1 Announce Type: new Abstract: Reinforcement learning post-training unlocks complex reasoning in LLMs. Yet benchmark scores reveal only whether a model improved, not what changed inside it, nor how it splits finite capability across tasks. A representative… 32 arXiv — NLP / Computation & Language research 2mo ago Embedding Initialization for Unseen Low-resource Languages in Multilingual NMT: A Case Study on Limbum-English Translation arXiv:2608.07629v1 Announce Type: new Abstract: Multilingual neural machine translation models such as NLLB-200 cover 200 languages but leave thousands unsupported, including most Grassfields Bantu languages of Cameroon. When fine-tuning these models for an unseen language,… 18 arXiv — NLP / Computation & Language research 2mo ago STEMMA: An Adversarial Multi-Agent Framework for Evaluating Self-Identity Consistency in LLMs arXiv:2608.08164v1 Announce Type: new Abstract: Knowledge Distillation is a widely adopted technique in the training and fine-tuning of large language models (LLMs) enabling transfer of structured information and functional behavior from a large teacher model to a smaller… 29 arXiv — NLP / Computation & Language research 2mo ago Can We Optimize the Performance-Carbon Emission Break-Even Point?: The Quest for Greener LLMs arXiv:2608.08744v1 Announce Type: new Abstract: The carbon footprint of any deployed Large Language Model (LLM) accumulates during inference, where repeated use of the model substantially exceeds the one-time cost of fine-tuning. Yet most efficiency interventions target either… 8 arXiv — NLP / Computation & Language research 2mo ago The Announcement Carries the Cue: Markup, Boundaries, and the Notation of Pre-Training Corpora arXiv:2608.09093v1 Announce Type: new Abstract: How a document's arrangement is written down, its notation, is a training variable that no dataset card records. The field has established that text-extraction choices change model behaviour, and has never once measured the… 35 arXiv — NLP / Computation & Language research 2mo ago Se-DPO: Self-Evolving Token Credit for Direct Preference Optimization arXiv:2608.09568v1 Announce Type: new Abstract: Direct Preference Optimization (DPO) aggregates token-level log-probability ratios via uniform summation, implicitly treating all tokens as contributing equally to the preference signal. However, the contribution of individual… 30 Page 8 of 10 · 500 articles ← Newer Older →
