News / #training Tag Training 500 articles archived under #training · RSS Sign in to follow arXiv — Machine Learning research 21d ago How Many Bits Can an Adapter Write? Measuring the Capacity and Memorization of Parameter-Efficient Fine-Tuning arXiv:2607.21351v1 Announce Type: new Abstract: A LoRA adapter is a few megabytes that almost everyone treats as a skill rather than a record of the data behind it. We put that assumption on a scale. Extending compression-based memorization analysis to the frozen-base setting,… 14 arXiv — Machine Learning research 21d ago Emergent Misalignment Recruits a Pre-existing Persona Subspace arXiv:2607.21356v1 Announce Type: new Abstract: Fine-tuning an aligned language model on a narrow stream of bad advice can make it broadly misaligned on questions unrelated to the training data, a phenomenon called emergent misalignment. We ask why the narrow lesson generalizes… 5 arXiv — NLP / Computation & Language research 21d ago Routing Subspaces: Auditing Evaluation-to-Deployment Mismatch in Fine-Tuned Language Models arXiv:2607.20436v1 Announce Type: new Abstract: Safety evaluations often assume that behavior observed during testing reflects behavior in ordinary use, but fine-tuning can break this assumption. A checkpoint can appear fixed under evaluation-style prompts while the same… 35 arXiv — NLP / Computation & Language research 21d ago Preference Tuning as Spectral Update Reorganization arXiv:2607.20438v1 Announce Type: new Abstract: Preference-based post-training is usually understood through endpoint behavior, yet the learned update that produces this behavior remains largely opaque. We study RLHF and related preference optimization through the spectral… 14 arXiv — NLP / Computation & Language research 21d ago Naver-News-KO: A Korean News Summarization Dataset for Open-Source Fine-Tuning of Summarization Models arXiv:2607.20442v1 Announce Type: new Abstract: We release Naver-News-KO, a Korean news summarization dataset of 27,400 (document, summary) pairs collected from Naver News over a ten-day window in July 2022 across two categories (Economy and IT/Science; 77/23 split), with… 8 arXiv — NLP / Computation & Language research 21d ago Domyn-Small: A European 10B Reasoning Language Model arXiv:2607.20448v1 Announce Type: new Abstract: We introduce Domyn-Small, a 10-billion-parameter open-weight reasoning language model released under the MIT license. Domyn-Small is the product of an initial pre-training phase on 9 trillion tokens multilingual data, followed by a… 32 arXiv — NLP / Computation & Language research 21d ago Learn2Zinc: Fine-tuning Small Language Models for Text-to-Model Translation in MiniZinc arXiv:2607.20456v1 Announce Type: new Abstract: Large language models excel at code generation for mainstream programming languages but struggle with rare, domain-specific languages such as MiniZinc, a constraint modeling language for combinatorial problems. We investigate… 33 arXiv — NLP / Computation & Language research 21d ago Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs arXiv:2607.21291v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong generation and reasoning performance, but the Transformer architecture incurs high inference cost. Existing acceleration methods often rely on task-specific fine-tuning or training from… 10 r/LocalLLaMA community 21d ago I compared local models and different quants / config on a subset of swe-verified bench And gathered a lot of data. you can see them for yourself And For the most curious, there are additional details here In this graph, I regrouped the finetunes under their base models. but you can see the details in the page. The python code to generate those pages is obviously… 18 arXiv — Machine Learning research 22d ago LAARA: Layer-Aware Adaptive Rank Allocation for Parameter-Efficient Fine-Tuning arXiv:2607.19391v1 Announce Type: new Abstract: Low-Rank Adaptation is widely used for parameter-efficient fine-tuning, yet existing methods typically assign the same adapter rank to every transformer layer despite their heterogeneous adaptation requirements. In this work, we… 35 arXiv — Machine Learning research 22d ago Predictive single cell foundation model for gene regulation and aging with privacy-preserving tabular learning arXiv:2607.19400v1 Announce Type: new Abstract: Pre-trained foundation models (FMs) have begun transforming single-cell genomics, but scaling them raises privacy concerns. Moreover, unlike text data, single-cell data is unordered and exhibits a unique tabular structure that… 17 arXiv — Machine Learning research 22d ago Reward-Aware Population Scaling of Evolutionary Strategies in LLM Fine-Tuning arXiv:2607.19408v1 Announce Type: new Abstract: Using Evolutionary Strategies (ES) for fine-tuning large language models is attractive because it is memory-efficient, parallel, and compatible with black-box or discrete rewards. Yet its population-size conclusions conflict… 8 arXiv — Machine Learning research 22d ago How Fast Can Reward Models Score? A Systems Study of C++ and PyTorch Inference Runtimes for RLHF arXiv:2607.19712v1 Announce Type: new 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… 4 arXiv — Machine Learning research 22d ago Evaluating and Mitigating Gender Bias in Pre-trained Embeddings for ML-based Recruitment arXiv:2607.20073v1 Announce Type: new Abstract: AI-based recruitment systems that rely on machine learning models trained on historical CV data, risk perpetuating and amplifying social biases. A key challenge arises in unstructured CV text, where pre-trained language model… 16 arXiv — NLP / Computation & Language research 22d ago The Blessing of Dimensionality: How Near-Orthogonality in High-Dimensional Spaces Explains Temporal Portability arXiv:2607.20301v1 Announce Type: cross Abstract: Fine-tuning has been widely used to adapt large language models (LLMs) for domain-specific tasks. Parameter efficient fine-tuning (PEFT) methods such as low-rank adaptation (LoRA) are frequently used to reduce computational… 24 arXiv — NLP / Computation & Language research 22d ago When Reasoning Narrows the Move: Diversity Collapse in LLM Game Play arXiv:2607.19523v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) is widely used to adapt large language models to downstream tasks, but its effect on behavioral diversity in sequential decision-making remains under-explored. We study this question in a controlled… 37 arXiv — NLP / Computation & Language research 22d ago TINY_SCHILLER: A Drop-In German Drama Corpus for Small Language Models arXiv:2607.19992v1 Announce Type: new Abstract: tiny_schiller closes the small-language-model prototyping, fine-tuning, education, and research gap for German literary text, providing a single-file, drop-in counterpart to Karpathy's tiny_shakespeare. The available German… 28 arXiv — NLP / Computation & Language research 22d ago Statistically Grounded Sparse-Feature Interventions for Activation-Space Control in Large Language Models arXiv:2607.19364v1 Announce Type: cross Abstract: Activation steering offers a lightweight alternative to fine-tuning for behavioral control of large language models, but SAE-based steering methods often rely on learned steering objectives or single-criterion feature selection.… 15 r/LocalLLaMA community 23d ago Tokenizer Expansion: Upgrading a Model's Tokenizer in Place - LFM2.5-8B-A1B Today, we're sharing the recipe behind the new tokenizer in LFM2.5-8B-A1B . It upgrades a pre-trained model's tokenizer in place , without retraining from scratch. We doubled the vocabulary from 65K to 128K to fix the languages our original tokenizer split too finely. Blog:… 34 TechCrunch — AI news-outlet 23d ago Glow emerges from stealth at $1.2B valuation to challenge endpoint security in the AI era Glow is targeting a new class of endpoint risks created by the rapid adoption of AI agents and developer tools inside enterprises. 4 arXiv — Machine Learning research 23d ago TD-DPO: Difference-Aware Preference Optimization for Mitigating Sycophancy in Clinical Autism Intervention Dialogue arXiv:2607.18304v1 Announce Type: new Abstract: The sycophancy of large language models can increase the safety risk in intervention dialogue for autistic children. Supervised fine-tuning can somewhat reduce sycophancy, but relying solely on positive examples is often… 21 arXiv — Machine Learning research 23d ago Federated Lightweight Fine-Tuning arXiv:2607.18343v1 Announce Type: new Abstract: Federated fine-tuning is bottlenecked by communication: FedAvg and pseudo-gradient schemes transmit a payload that scales with the model, and gradient compression shrinks it by only a constant factor. We take a different lever.… 23 arXiv — Machine Learning research 23d ago SFGA: A Statistics-First Gating Architecture with Adjudicative Escalation for Trustworthy SFT Data Procurement arXiv:2607.18960v1 Announce Type: new Abstract: Procuring supervised fine-tuning (SFT) data forces a buyer to decide, before any downstream training, whether a candidate corpus is worth acquiring. We present \sys{}, a statistics-first gating architecture that treats procurement… 19 arXiv — Machine Learning research 23d ago Adopting Reinforcement Learning with Verifiable Rewards for Molecular Generation arXiv:2607.19044v1 Announce Type: new Abstract: Leveraging large language models (LLMs) for molecular generation has shown remarkable potential in chemical and drug design. Current methods primarily rely on supervised training or fine-tuning with limited datasets, which are… 16 arXiv — NLP / Computation & Language research 23d ago Reasoning Fine-Tuning Induces Persistent Latent Policy States arXiv:2607.18532v1 Announce Type: new Abstract: Reasoning-specialized language models show large performance gains over base models, yet the internal changes responsible for improved multi-step reasoning remain poorly understood. It is unclear whether reasoning fine-tuning… 15 arXiv — NLP / Computation & Language research 23d ago Find Before You Fine-Tune: A Diagnostic Study of Small LLMs for Cybersecurity QA arXiv:2607.18725v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly fine-tuned for critical-domain Question-Answering (QA), yet choosing which small model to adapt, before paying the cost of adaptation, remains difficult. Fine-tuning can improve domain… 7 arXiv — NLP / Computation & Language research 23d ago Beyond Score Prediction: LLM-Based Essay Scoring and Feedback Generation via Reinforcement Learning with Rubric Rewards arXiv:2607.19219v1 Announce Type: new Abstract: Large language models (LLMs) have been widely applied to automated essay scoring (AES) and automated feedback generation (AFG). However, existing studies rely primarily on prompt engineering or supervised fine-tuning, while… 18 arXiv — NLP / Computation & Language research 23d ago CircuitKIT : Circuit Discovery, Evaluation, and Application Toolkit for Mechanistic Interpretability arXiv:2607.19317v1 Announce Type: cross Abstract: Circuit analysis can support not only model explanation but also downstream interventions such as pruning, editing, steering, and selective fine-tuning. However, conducting such analyses currently requires stitching together… 15 r/LocalLLaMA community 23d ago 20B Looping model (paper) matches or beats Qwen3 Coder 30B at 10% of pre-training tokens No weights yet. I feel sad for them, that training run cost maybe 100s of thousands of dollars and they didn't even beat GPT-OSS 20B in every regard But the ability to train a model from scratch on 3.5 trillion tokens instead of 35 trillion sure gives me hope. They only spent… 33 NVIDIA Developer Blog official-blog 23d ago Setting a World Record for MoE Pre-Training on NVIDIA GB300 NVL72 Frontier model pre-training has converged on mixture of experts (MoE), which is fundamentally changing what limits large-scale AI training. As compute per token... 23 arXiv — Machine Learning research 24d ago Normalized Rewards for Preference Optimization arXiv:2607.16240v1 Announce Type: new Abstract: Direct Alignment Algorithms (DAAs) such as DPO have become a common way to post-train and align LLMs with human preferences. However, DAAs have been observed to over-optimize their implicit reward model and decrease the likelihood… 20 arXiv — Machine Learning research 24d ago TRACE: Trajectory-Based Safety Patch Learning for LLM Post-Training Realignment arXiv:2607.16242v1 Announce Type: new Abstract: Fine-Tuning-as-a-Service (FTaaS) platforms let users train large language models (LLMs) on customized tasks, but this pipeline could erode models' safety alignment. In practice, service providers need to recover models' safety… 32 arXiv — Machine Learning research 24d ago Let the Data Decide: Supervision Analysis, Capability Trade-offs, and Adaptive Objective Routing in Continued Pre-Training via Off-Policy Distillation arXiv:2607.16246v1 Announce Type: new Abstract: Off-policy distillation is now central to large language model pre-training, yet how training data, objective parameterization, and model capabilities interact remains poorly characterized. We studies top-$k$-truncated,… 14 arXiv — Machine Learning research 24d ago Learning Spatio-Temporal Foundation Models from Pure Synthetic Data arXiv:2607.16251v1 Announce Type: new Abstract: Spatio-Temporal Foundation Models (STFMs) aim to learn generalizable representations of complex dynamical systems across space and time. However, existing approaches suffer from distributional bias in real-world pre-training data,… 20 arXiv — Machine Learning research 24d ago SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling arXiv:2607.16252v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) is a widely used parameter-efficient fine-tuning (PEFT) method for large language models. Under a fixed rank budget, LoRA parameterizes each adapted weight through a single low-dimensional input-side… 13 arXiv — Machine Learning research 24d ago MultiLoReFT: Decoupling Shared and Modality-Specific Subspaces in Multimodal Learning via Low-Rank Representation Fine-Tuning arXiv:2607.16789v1 Announce Type: new Abstract: Real-world perception and decision making are inherently multimodal, integrating complementary signals across modalities. However, training multimodal models faces two main obstacles. First, collecting large-scale, well-aligned… 5 arXiv — Machine Learning research 24d ago First-Order Predictable but Pairwise Fragile: Local Task Adaptation in Trained Transformers arXiv:2607.16821v1 Announce Type: new Abstract: Task arithmetic, sequential fine-tuning, activation steering, and first-order random search all operate through relatively small perturbations around an already trained checkpoint, and they rely on different local approximations:… 11 arXiv — Machine Learning research 24d ago Robust Assamese Speech Recognition through Controlled Fine-Tuning of Whisper Models arXiv:2607.17164v1 Announce Type: new Abstract: Developing Automatic Speech Recognition (ASR) for morphologically rich, low-resource languages such as Assamese is challenging due to insufficient annotated speech data. The pretrained Whisper model performs poorly on Assamese… 16 arXiv — NLP / Computation & Language research 24d ago Schema-Constrained Document-Level Event Argument Extraction with Lightweight LLM Fine-Tuning arXiv:2607.16808v1 Announce Type: new Abstract: Event Argument Extraction (EAE) converts documents into structured event records by identifying argument spans and assigning them schema-defined roles. Document-level EAE is challenging due to long-range dependencies between… 22 arXiv — NLP / Computation & Language research 24d ago Trace-Based On-Policy Distillation for Masked Diffusion Language Models arXiv:2607.16872v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) are a promising alternative to autoregressive generation. However, reasoning-oriented post-training for dLLMs remains challenging. Supervised fine-tuning (SFT) for dLLMs requires dense but… 14 arXiv — NLP / Computation & Language research 24d ago What Transfers Under Source Shift? Definitions, Examples, and Fine-Tuning for Climate Disclosure Classification arXiv:2607.17952v1 Announce Type: new Abstract: Climate disclosure classification is a fundamental task for analysing corporate climate disclosures, yet such disclosures appear in many different sources -- annual reports, press releases, and earnings calls -- that differ in… 28 arXiv — NLP / Computation & Language research 24d ago PPL-Factory: Task-Aware and Budget-Aware Data Selection from Language Modeling to Reasoning arXiv:2607.18199v1 Announce Type: new Abstract: Not all training samples contribute equally to large language model fine-tuning. Selecting informative training samples can reduce the computational cost while preserving downstream performance. Many existing data selection methods… 25 r/LocalLLaMA community 24d ago Unsloth now supports AMD! Hey r/LocalLLaMA folks! Unsloth now officially supports AMD hardware for local inference, fine-tuning, reinforcement learning, and deployment! It's been in the works for quite some time, but it works on Windows, Linux & WSL devices (+ technically Mac) with AMD GPUs! Unsloth… 24 Hugging Face Daily Papers research 25d ago When Does Muon Help Agentic Reinforcement Learning? Abstract Muon is competitive with AdamW in large-scale pre-training, but its value for reinforcement-learning (RL) post-training remains unclear. We study vanilla Muon in sparse-reward agentic RL through matched single-seed comparisons with AdamW on ALFWorld using… 14 arXiv — Machine Learning research 25d ago When Does Muon Help Agentic Reinforcement Learning? arXiv:2607.16169v1 Announce Type: new Abstract: Muon is competitive with AdamW in large-scale pre-training, but its value for reinforcement-learning (RL) post-training remains unclear. We study vanilla Muon in sparse-reward agentic RL through matched single-seed comparisons with… 10 arXiv — NLP / Computation & Language research 25d ago Analysing Moral Bias in Finetuned LLMs through Mechanistic Interpretability arXiv:2510.12229v3 Announce Type: replace Abstract: Large language models (LLMs) have been shown to internalize human-like biases during finetuning, yet the mechanisms by which these biases manifest remain unclear. In this work, we investigated whether the well-known Knobe… 22 arXiv — NLP / Computation & Language research 25d ago RLearner-LLM: Balancing Logical Grounding and Fluency in Large Language Models via Hybrid Direct Preference Optimization arXiv:2605.04539v4 Announce Type: replace Abstract: Direct Preference Optimization (DPO), the efficient alternative to PPO-based RLHF, falls short on knowledge-intensive generation: standard preference signals from human annotators or LLM judges exhibit a systematic verbosity… 29 r/LocalLLaMA community 26d ago I tried fine-tuning a ternary model, Bonsai 8b, on metal We've heard the hype around binary and ternary models, and it seems recently, their performance is starting to catch up and be competitive for every-day use across a range of devices, although there's still more to wish for... What these models are A new family of true sub-2-bit… 13 arXiv — Machine Learning research 28d ago CARPRT: Class-Aware Zero-Shot Prompt Reweighting for Black-Box Vision-Language Models arXiv:2607.14125v1 Announce Type: new Abstract: Pre-trained vision-language models (VLMs) enable zero-shot image classification by computing the similarity score between an image and textual descriptions, typically formed by inserting a class label (e.g., "cat") into a prompt… 19 arXiv — Machine Learning research 28d ago Dysco: Dynamic Subspace Boosting to Mitigate LoRA Interference in Federated Learning arXiv:2607.14367v1 Announce Type: new Abstract: Federated fine-tuning of large pre-trained models increasingly relies on Low-Rank Adaptation (LoRA) to reduce communication and computation, but heterogeneous clients can make adapter aggregation unstable. We identify the… 37 Page 4 of 10 · 500 articles ← Newer Older →
