News / #training Tag Training 500 articles archived under #training · RSS Sign in to follow arXiv — Machine Learning research 2mo ago A Lightweight Foundation Model for Collider Physics with Multi-Domain Adaptation arXiv:2607.27501v1 Announce Type: new Abstract: We present a lightweight approach to foundation modeling (\textbf{NEXUS}) that leverages pre-trained learning from collider physics data towards out-of-domain tasks in other scientific datasets, using a fully connected autoencoder… 33 arXiv — NLP / Computation & Language research 2mo ago Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection arXiv:2607.27680v1 Announce Type: cross Abstract: Low-Rank Adaptation (LoRA) has become the standard mechanism for fine-tuning large pretrained models, yet its statistical properties remain only partially understood. Existing generalization results provide upper bounds of the… 22 arXiv — Machine Learning research 2mo ago VESTIGE: A Knowledge-Guided Masking Strategy for Corruption-Aware Fine-Tuning of Genomic Transformers, Validated on Ancient DNA Reconstruction arXiv:2607.27712v1 Announce Type: new Abstract: Standard masked-language-model fine-tuning applies a uniform masking probability across every token position, assuming reconstruction difficulty is position-agnostic. When the degradation process is characterised and concentrated… 4 arXiv — Machine Learning research 2mo ago Exact Action Values Are Not Enough: Rollout-Verified Reinforcement Fine-Tuning of a Reasoning Model for Multi-Zone VAV Control arXiv:2607.27914v1 Announce Type: new Abstract: Multi-zone variable-air-volume control must balance thermal comfort, indoor air quality, and electricity use across several continuous actuators. Model predictive control and reinforcement learning are widely studied, but… 31 arXiv — Machine Learning research 2mo ago Harnessing the Potential of Optimizing Data Mixtures via Bayesian Domain Reweighting arXiv:2607.27928v1 Announce Type: new Abstract: The performance of Large Language Models (LLMs) is fundamentally influenced by the distributional composition of multi-domain pre-training data. While manual heuristics were prevalent in early models, they increasingly fail to… 21 arXiv — NLP / Computation & Language research 2mo ago TriShield: Zero-Utility-Loss Defense Against Privacy Backdoors in Federated Language Model Fine-Tuning via Orthogonal Gradient Projection and Optimizer State Entanglement arXiv:2607.27940v1 Announce Type: cross Abstract: Federated fine-tuning of large language models (LLMs) enables collaborative training without exposing raw data. However, a recent attack, NeuroImprint [1] (arXiv:2606.20553), demonstrates that a malicious parameter server can… 31 arXiv — Machine Learning research 2mo ago HARGO: Heterogeneity-Aware Reward-Guided Optimization for RL Post-Training of LLMs on HPC Tasks arXiv:2607.28301v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) can equip large language models (LLMs) with domain knowledge for high-performance computing (HPC) tasks such as data race detection and benchmark question answering. However, knowledge alone does not… 23 arXiv — NLP / Computation & Language research 2mo ago CDAE: Enhancing Perturbation Robustness in Pretrained Language Models with Contrastive Denoising arXiv:2607.28236v1 Announce Type: cross Abstract: Pre-trained language models have significantly improved sentence representation learning, yet their embedding remain sensitive to semantic preserving textual perturbations such as synonym substitution, masking and word dropout.… 19 r/LocalLLaMA community 2mo ago Making a synthetic dataset for fine-tuning I've been thinking about building a pipeline to generate reasoning training data for LLMs, but I want to avoid the common failure mode of synthetic data where you just generate the same template with different numbers. The rough idea: Generate an abstract reasoning task (logic,… 9 r/LocalLLaMA community 2mo ago Nanbeige4.2-3B: I'm not impressed I've tested Nanbeige-4.2-3B. On paper, the benchmarks promise it blows away Qwen3.5-9B and Gemma4-12B. My goal was to have something very light and fast to replace Qwen3.6-35B (or finetunes thereof) for simple and straightforward coding tasks. In the past I tried downgrading… 35 Hugging Face Daily Papers research 2mo ago CLBench-V: Evaluating Multimodal Context Learning from Grounding to Knowledge Acquisition Abstract Real-world tasks often require models to learn from task-specific context rather than relying only on pre-trained knowledge. While recent work has highlighted this capability as context learning, existing evaluations mainly focus on textual contexts. In many practical… 14 arXiv — NLP / Computation & Language research 2mo ago Meta-Learned Reward Shaping for Reinforcement Learning from Human Feedback arXiv:2607.26094v1 Announce Type: cross Abstract: Reinforcement Learning from Human Feedback (RLHF) is the standard approach for aligning large language models with human preferences, but its quality is limited by static, task-agnostic reward models. This mismatch leads to… 22 arXiv — Machine Learning research 2mo ago Shared SFT Lessons Across Alignment, Model Organisms, and Toy Models arXiv:2607.26173v1 Announce Type: new Abstract: Alignment training, model organisms, and toy models are usually treated as separate research areas. But projects in all three frequently use supervised fine-tuning (SFT) to pursue the same underlying goals. When projects share a… 5 arXiv — Machine Learning research 2mo ago Between Gradient and Natural Gradient: A Continuum of LoRA Initializations arXiv:2607.26247v1 Announce Type: new Abstract: Low-rank adaptation (LoRA) fine-tunes large pretrained models at a fraction of the cost of full fine-tuning, but its performance depends strongly on how the adapters are initialized. Recent schemes initialize the adapters from the… 23 arXiv — Machine Learning research 2mo ago Post-Training at the Edge of Detectability: A Game-Theoretic Approach to Fine-Tuning arXiv:2607.26358v1 Announce Type: new Abstract: Reinforcement learning (RL) fine-tuning is widely used in language model training to improve model performance on a target task while limiting drift from a reference policy. A standard way to balance this trade-off is via a… 6 arXiv — Machine Learning research 2mo ago Do You Really Need to Pretrain Q-Functions for Online RL Fine-Tuning? arXiv:2607.27203v1 Announce Type: new Abstract: Pre-training followed by fine-tuning has become the dominant recipe for learning performant policies, and in value-based reinforcement learning (RL) this raises a natural question: given a pretrained policy, should the Q-function… 37 arXiv — NLP / Computation & Language research 2mo ago Misalignment Has a Personality: A Big Five Account of Emergent Misalignment arXiv:2607.26389v1 Announce Type: new Abstract: Fine-tuning a language model on data containing a narrow flaw, such as insecure code or incorrect mathematical answers, can cause broad misalignment through a mechanism that remains debated. We provide an interpretable account: in… 13 arXiv — NLP / Computation & Language research 2mo ago Constitutional Midtraining: Content Presence Drives Alignment Gains arXiv:2607.26654v1 Announce Type: new Abstract: Post-training alignment is often shallow, eroding under fine-tuning. Whether midtraining interventions, cleanly isolated from post-training, can produce durable alignment remains untested. We test this via constitutional… 31 arXiv — NLP / Computation & Language research 2mo ago On-Policy Distillation for LLM Safety: A Routing Approach to Template-Robust Realignment arXiv:2607.27081v1 Announce Type: cross Abstract: Fine-tuning is the dominant paradigm for specializing large language models (LLMs), yet it exposes a critical vulnerability: malicious data providers can embed harmful behaviors into downstream corpora, creating models that… 5 arXiv — NLP / Computation & Language research 2mo ago Ensembling LLM-Induced Decision Trees for Explainable and Robust Error Detection arXiv:2512.07246v3 Announce Type: replace Abstract: Error detection (ED), which aims to identify incorrect or inconsistent cell values in tabular data, is important for ensuring data quality. Recent state-of-the-art ED methods leverage the pre-trained knowledge and semantic… 21 Hugging Face Daily Papers research 2mo ago How Fast Can Reward Models Score? A Systems Study of C++ and PyTorch Inference Runtimes for RLHF Abstract In RLHF pipelines, reward scoring blocks policy updates. Slow scoring bottlenecks the entire loop, since no update runs until every rollout gets a score. And yet most setups just default to PyTorch eager mode or torch.compile, no one checks if that's actually fastest.… 26 r/LocalLLaMA community 2mo ago I pre-trained a 700m on 18B tokens optimized for Python and Wikitext | TheOneWhoWill/Shibai-700M-Base · Hugging Face I know this is the 1000000th new sub billion parameter model out there and probably isn't as good as Qwen 3 0.6B or Qwen 3.5 0.8B but it still packs a decent punch. My intention to to continuously pre-train this model on another 5 Billion tokens or so on pure doc string based… 9 Hugging Face Daily Papers research 2mo ago OPERA: Offline Policy-guided Expert Routing and Adaptation for Universal Biomedical Image Analysis Abstract Biomedical image analysis spans diverse modalities and tasks, yet real-world deployment is hindered by severe distribution shifts across scanners, protocols, and patient populations. High-performing models consequently require repeated domain-specific fine-tuning, which… 14 r/LocalLLaMA community 2mo ago Built and released BetterGPT-150M – A compact 150M parameter completion model (+ live HF Space demo) Hey everyone, I recently finished pre-training BetterGPT-150M, a small, lightweight causal language model with ~152 million parameters.Trained on 15B tokens. Dataset & Training: Trained across stable and annealing phases using curated datasets (including FineWeb-Edu, fine… 16 arXiv — NLP / Computation & Language research 2mo ago Detecting Knowledge Inconsistencies Across Text, Tables, and Knowledge Graphs arXiv:2607.25959v1 Announce Type: new Abstract: Wikipedia and Wikidata are widely used for information access, LLM pre-training, and retrieval-augmented generation. Their knowledge is deeply connected but scattered across text, tables, and knowledge graphs. This raises a… 28 arXiv — NLP / Computation & Language research 2mo ago Influence of Prompt Engineering on Small Language Models for Guarded Query Routing arXiv:2607.24801v1 Announce Type: cross Abstract: We study the problem of guarded query routing, where we assume that a user query first meets a router that either determines the ideal endpoint for in-distribution queries or rejects out-of-distribution queries that are… 17 arXiv — NLP / Computation & Language research 2mo ago CLBench-V: Evaluating Multimodal Context Learning from Grounding to Knowledge Acquisition arXiv:2607.25294v1 Announce Type: cross Abstract: Real-world tasks often require models to learn from task-specific context rather than relying only on pre-trained knowledge. While recent work has highlighted this capability as context learning, existing evaluations mainly focus… 4 arXiv — NLP / Computation & Language research 2mo ago MemSFT: Mitigating Alignment Tax with an External Parametric Memory arXiv:2607.25614v1 Announce Type: cross Abstract: Adapting Large Language Models (LLMs) to specialized domains often incurs an alignment tax, as fine-tuning on domain-specific tasks can cause catastrophic forgetting and substantially degrade performance on general tasks. We… 37 r/LocalLLaMA community 2mo ago Medical model: Reasoning-Medical-27B (Qwen3.6-27B finetune) From the description: "Reasoning-Medical-27B is designed for universal advanced medical reasoning in professional medicine, medical genetics, college biology/medicine, and clinical knowledge. The model was fine-tuned on a large-scale dataset of 370,000 high-quality… 38 Hugging Face Daily Papers research 2mo ago The Physics of Multi-Turn Long-Horizon Planning: From Pre-training to Post-training via Single- and Multi-Teacher On-Policy Agentic Distillation Abstract Multi-turn long-horizon planning is critical for foundation model agents, yet how to fundamentally improve it remains unclear. Existing models are trained on uncontrollable and opaque Internet data, making it difficult to identify how planning ability is acquired,… 33 arXiv — Machine Learning research 2mo ago QFedPolyp: A Communication- and Inference-Efficient Federated Learning Framework for Polyp Segmentation arXiv:2607.22743v1 Announce Type: new Abstract: Background and Objective: Automatic polyp segmentation supports computer-aided diagnosis and early colorectal cancer detec- tion. Centralized deep learning requires hospitals to share sensitive medical data, while federated… 30 arXiv — Machine Learning research 2mo ago DomainPilot: Domain-Level Loss-Guided Two-Stage Data Mixture Optimization for Efficient Language Model Fine-Tuning arXiv:2607.22769v1 Announce Type: new Abstract: The training efficacy of large language models (LLMs) is fundamentally constrained by the quality and composition of training data. Existing dynamic data scheduling methods face critical limitations in industrial-scale pretraining… 19 arXiv — Machine Learning research 2mo ago Distribution-Specific Curvature Control with Finite-Sample Guarantees for Open-Weight Safety arXiv:2607.22929v1 Announce Type: new Abstract: A short fine-tuning run can undo the safety guards of an open-weight model---retraining a refusal-trained assistant to aid weapons development or produce hate speech. Preventing such harmful fine-tuning while retaining benign… 30 arXiv — Machine Learning research 2mo ago Foundation Models and Fine-Tuning: Toward a New Generation of Models for Time Series Forecasting arXiv:2607.23146v1 Announce Type: new Abstract: Inspired by recent breakthroughs in large language models for natural language processing, foundation models have emerged as a promising paradigm for zero-shot time series forecasting, enabling accurate predictions on datasets… 17 arXiv — Machine Learning research 2mo ago StageGuard: Physiologically Constrained Sleep Staging arXiv:2607.23284v1 Announce Type: new Abstract: Automated sleep staging is increasingly used in large-scale studies to derive sleep-architecture endpoints: total sleep time, REM latency, sleep efficiency, and bout-duration statistics. Deep learning models achieve epoch-level… 8 arXiv — NLP / Computation & Language research 2mo ago Learning When to Reason for Text-to-SQL via SFT and DPO arXiv:2607.22622v1 Announce Type: new Abstract: Recent Text-to-SQL methods rely heavily on reasoning-centric paradigms such as Chain-of-Thought (CoT), achieving substantial gains on complex benchmarks at the cost of high inference-time overhead. However, a large fraction of… 36 arXiv — NLP / Computation & Language research 2mo ago Do Small Models Use the Law You Give Them? Context-Injected Fine-Tuning for Legal QA in Bangladesh arXiv:2607.23446v1 Announce Type: new Abstract: A small language model can receive the governing statutory provision and still answer incorrectly. We test whether fine-tuning on examples containing relevant law improves later use of retrieved law. We curate 2{,}165 bilingual QA… 31 arXiv — NLP / Computation & Language research 2mo ago The Physics of Multi-Turn Long-Horizon Planning: From Pre-training to Post-training via Single- and Multi-Teacher On-Policy Agentic Distillation arXiv:2607.24720v1 Announce Type: new Abstract: Multi-turn long-horizon planning is critical for foundation model agents, yet how to fundamentally improve it remains unclear. Existing models are trained on uncontrollable and opaque Internet data, making it difficult to identify… 8 arXiv — NLP / Computation & Language research 2mo ago STAIF: A Stage-wise Optimization for Complex Instruction Following arXiv:2607.22649v1 Announce Type: cross Abstract: Following complex instructions with multiple explicit constraints remains a fundamental challenge for large language models (LLMs). Existing alignment methods, such as DPO, optimize holistic reward signals that often… 17 Hugging Face Daily Papers research 2mo ago Scaling Native Multimodal Pre-Training From Scratch Abstract Although large language models (LLMs) exhibit remarkable reasoning capabilities, their reliance on text-only pre-training restricts the perception of the multimodal physical world. Native multimodal pre-training avoids this limitation by training models from scratch on… 28 arXiv — Machine Learning research 2mo ago Encoding Invisible Causation for Bridge Diagnostic Agents: Triple-Guided Retrieval-Augmented Fine-Tuning with QLoRA arXiv:2607.21680v1 Announce Type: new Abstract: Bridge infrastructure deteriorates gradually, yet its root causes---salt intrusion, freezing, fatigue cracking, and others---remain invisible to the naked eye. Expert diagnosis relies on tacit knowledge built over years of… 21 arXiv — Machine Learning research 2mo ago IFCLoRA: Topology-Aware Rank Allocation for Parameter-Efficient Fine-Tuning arXiv:2607.22251v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) is a widely used parameter-efficient fine-tuning method for large language models, but its performance depends strongly on how a fixed rank budget is distributed across Transformer modules. Existing… 4 arXiv — Machine Learning research 2mo ago Beyond Negative-Ridge Endpoints: Mixed-Sign Spectral Regularization via Negative-Shifted Gradient Descent arXiv:2607.22474v1 Announce Type: new Abstract: In overparameterized linear regression, many weak spectral directions act like a ridge penalty on the signal-bearing spectrum; negative ridge is the natural correction, pushing filters above one. The stable negative-ridge endpoint,… 25 arXiv — Machine Learning research 2mo ago \k{appa}-LoRA: Condition Numbers Reveal Which LoRA Matrices Worth Updating arXiv:2607.22489v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) has become a widely adopted technique for efficient neural network fine-tuning, decomposing model updates into low-rank matrices. However, LoRA remains computationally costly because it updates all… 4 arXiv — Machine Learning research 2mo ago Procedural Knowledge Is Not Low-Rank: Why LoRA Fails to Internalize Multi-Step Procedures arXiv:2607.21612v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning methods like LoRA have become the default for adapting large language models, succeeding across instruction following, style transfer, and factual adaptation. We show that for procedural… 22 arXiv — Machine Learning research 2mo ago FBLayout: Optimizing Memory Layout for Efficient LLM Finetuning on Mobile GPUs arXiv:2607.21624v1 Announce Type: cross Abstract: Transformer-based models have enabled unprecedented capabilities across language, vision, and multimodal tasks. On-device fine-tuning of transformer models offers a privacy-preserving path to personalized AI, yet remains… 20 arXiv — Machine Learning research 2mo ago Longitudinal Random Forests for Sparse and Irregular Response Trajectories arXiv:2607.21817v1 Announce Type: cross Abstract: Longitudinal studies often collect data at sparse, irregular, and unequally spaced time points. Such heterogeneity is often driven by subject-specific covariates, yet existing methods have been restricted to a scalar endpoint… 31 arXiv — NLP / Computation & Language research 2mo ago MoE$^2$-LoRA: When MoE Models Meet MoE-style Low-Rank Adaptation arXiv:2607.21978v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures have been widely adopted in large language models, yet parameter-efficient fine-tuning (PEFT) for MoE models remains underexplored. Existing PEFT methods for MoE either ignore router priors… 12 arXiv — NLP / Computation & Language research 2mo ago Scaling Native Multimodal Pre-Training From Scratch arXiv:2607.22043v1 Announce Type: new Abstract: Although large language models (LLMs) exhibit remarkable reasoning capabilities, their reliance on text-only pre-training restricts the perception of the multimodal physical world. Native multimodal pre-training avoids this… 31 arXiv — NLP / Computation & Language research 2mo ago Benchmarking Fine-tuning and Retrieval Strategies for a Multimodal Language Model on the NRC Reactor Operator Licensing Examination arXiv:2607.22067v1 Announce Type: new Abstract: The integration of large language models (LLMs) into the nuclear power industry requires outputs grounded in domain-specific knowledge. This study evaluates a 31-billion-parameter open-weight multimodal model (Gemma 4 31B-IT) on… 26 Page 10 of 10 · 500 articles ← Newer
