News / #training Tag Training 500 articles archived under #training · RSS Sign in to follow r/LocalLLaMA community 21h ago Were designing a tiny autonomous research agent This base model is only 43m parameters trained on 3m arXiv abstracts. We plan to continue pre-training and post training. If you create fine-tuning datasets or if you know of any datasets that can help shape the behavior for our goal we appreciate all contributors. The goal is… 11 arXiv — Machine Learning research 2d ago Disentangling Optimization Scale from Preference Scale in DPO arXiv:2608.27032v1 Announce Type: new Abstract: Direct Preference Optimization (DPO) is a widely used objective for aligning language models from preference data, with the coefficient $\beta$ commonly interpreted as controlling the KL constraint to a reference policy. We show… 25 arXiv — NLP / Computation & Language research 2d ago SPT: Skills as Pre-Training Data for Agentic Language Models arXiv:2608.26563v1 Announce Type: new Abstract: Agentic (tool-using) language models are mainly trained on tool-call traces and agent trajectories during post-training. These data provide direct behavioral supervision, but producing them requires task environments, execution,… 13 arXiv — NLP / Computation & Language research 2d ago TabuLM: Morphology-Aware Tabular Pre-training for Low-Resource Languages arXiv:2608.26923v1 Announce Type: new Abstract: We present TabuLM, the first language model pre-trained on Kinyarwanda tabular data. Kinyarwanda is a morphologically rich Bantu language spoken by over 12 million people in Rwanda, yet lacks any dedicated tabular representation… 25 arXiv — NLP / Computation & Language research 2d ago Squeezing More from Limited Data with Recursive Transformers arXiv:2608.26973v1 Announce Type: new Abstract: Pre-training under limited data requires a different view of scaling than web-scale language modeling. With a fixed data budget but relatively abundant compute, increasing parameter count helps only up to an optimal scale; beyond… 27 r/LocalLLaMA community 2d ago Appreciation Post - thomsonreuters/Thomson-1.0-Small With the lack of support from Qwen regarding the smaller 9B and 35B MOE models. Like myself, not everyone is looking for an agentic coding model, I particularly use it for RAG and reviewing and require high reasoning across different documents & came across this Finetune:… 22 Hugging Face Daily Papers research 2d ago LibriBrain100: One Hundred Hours of Broad and Deep MEG Data for Neural Speech Decoding at Scale Abstract LibriBrain100 is a large-scale MEG speech dataset that demonstrates improved decoding through extensive within-subject recordings and multi-subject supervised fine-tuning of pre-trained models. Generated by thinkingmachines/Inkling-Small We introduce LibriBrain100, a… 12 Hugging Face Daily Papers research 3d ago Is Next-Chunk Reasoning RL Really Better than SFT? Revisiting Training Strategies under no-CoT Data Abstract Mixed supervised fine-tuning on combined reasoning corpora outperforms next-chunk reinforcement learning in efficiency and final accuracy across mathematical and out-of-domain tasks. Generated by thinkingmachines/Inkling-Small Recent work proposes next-chunk reasoning… 18 arXiv — Machine Learning research 3d ago Resource-Efficient Pruning for Transformer via Low-Rank Importance Estimation arXiv:2608.24973v1 Announce Type: new Abstract: With the rapid development of large-scale pre-trained language models based on Transformer architectures, their high computational and memory costs have become a major obstacle to deployment, especially in resource-constrained… 30 arXiv — Machine Learning research 3d ago Mitigating LLM sycophancy with RL-based fine-tuning: Bayesian Truth Serum approach arXiv:2608.25267v1 Announce Type: new Abstract: Large language models (LLMs) frequently exhibit \emph{sycophancy}: they adapt their answers to a user's stated beliefs or preferences instead of reporting what they hold to be true, which lowers factual accuracy and can amplify… 22 arXiv — Machine Learning research 3d ago Fairness-Aware Test-Time Prompt Tuning arXiv:2608.25707v1 Announce Type: new Abstract: Vision-language models have displayed remarkable capabilities in multi-modal understanding and are increasingly used in critical applications where economic and practical deployment constraints prohibit re-training or fine-tuning.… 25 arXiv — Machine Learning research 3d ago TailSFT: Filtered Fine-Tuning Improves Post-Training Performance arXiv:2608.25756v1 Announce Type: new Abstract: Reinforcement learning post-training drives reasoning and agentic capabilities in modern AI systems, yet a growing body of work shows that it is most effective when used to fine-tune an already capable base model. We question… 37 arXiv — NLP / Computation & Language research 3d ago Does Fine-Tuning Undo Activation Steering? Behavioural Recovery Without Weight-Edit Reversal arXiv:2608.24988v1 Announce Type: new Abstract: Activation steering can be embedded directly into a language model's weights, shaping behaviour without inference-time intervention and offering a way to encode alignment prior to release. However, models are routinely fine-tuned… 28 arXiv — NLP / Computation & Language research 3d ago From Memorization to Absorption: Mixed-Policy RL for Continual Knowledge Injection arXiv:2608.25243v1 Announce Type: new Abstract: Continual knowledge injection is essential for keeping large language models up-to-date in a fast-evolving world. Existing methods rely on supervised fine-tuning (SFT), which memorizes injected facts in their training format but… 23 arXiv — NLP / Computation & Language research 3d ago Cross-Dataset Stability of Expert-Informed Skill Prompting and Fine-Tuning for Chinese Metaphor Identification arXiv:2608.25579v1 Announce Type: new Abstract: Metaphor-identification performance can change markedly across datasets that differ in text distribution and annotation policy. We examine whether a fixed expert-informed procedure produces a more even cross-dataset profile than… 7 arXiv — NLP / Computation & Language research 3d ago Learning New Facts with QLoRA: An Acquisition-Retention Frontier arXiv:2608.25677v1 Announce Type: new Abstract: Parameter-efficient fine-tuning is often assumed to preserve pretrained capabilities because it updates only a small number of parameters. We show that this assumption depends strongly on adapter capacity. We study factual… 23 arXiv — NLP / Computation & Language research 3d ago Unfolding Scientific Papers into Multi-Turn Generation Trajectories for Continued Pre-Training arXiv:2608.25826v1 Announce Type: new Abstract: A recent line of synthetic-data work reconstructs the thinking behind existing text rather than rewriting the text itself, but it operates on short web passages, recovers only local thoughts, and leaves the structure of whole… 12 arXiv — NLP / Computation & Language research 3d ago Loss-Based Active Learning for Neural Abstractive Summarization arXiv:2608.25881v1 Announce Type: new Abstract: Fine-tuning abstractive summarization models requires high-quality annotated data. However, obtaining such corpora is expensive and time-consuming, as it requires human annotators to read and comprehend long documents to create… 8 arXiv — NLP / Computation & Language research 3d ago Fine-Tuning Whisper for Automatic Speech Recognition in Baniwa: A Preliminary Study arXiv:2608.26060v1 Announce Type: new Abstract: Automatic Speech Recognition (ASR) technologies have achieved remarkable performance in recent years through the use of large multilingual foundation models. However, most advances remain concentrated on high-resource languages,… 9 Anthropic SDK (Python) releases dev-tools 3d ago v1.1.0 1.1.0 (2026-08-26) Full Changelog: v1.0.0...v1.1.0 Features api: add updates thinking display mode (beta) ( eb4a73f ) api: add missing anthropic-beta values ( dbebd15 ) api: add support for Organization API endpoints ( 5a5b8fc ) Bug Fixes docs: correct link for long requests… 31 Hugging Face Daily Papers research 3d ago SecOPD: Mitigating Adaptive Prompt Injections by On-Policy Distillation Abstract SecOPD improves defense against adaptive prompt injection by using token-level feedback during fine-tuning, sharply reducing attack success rates on language models. Generated by thinkingmachines/Inkling-Small Prompt injection is listed as the \#1 threat to AI agents.… 18 Hugging Face Daily Papers research 4d ago LAION-BVD: A 10-Million-Hour Open Video Dataset for Multimodal Pre-training Abstract LAION-BVD is a large-scale open video dataset enabling multimodal pre-training across video, audio, and image modalities with synthetic captions and strong benchmark performance. Generated by thinkingmachines/Inkling-Small We present LAION-BVD, a large-scale open video… 22 arXiv — Machine Learning research 4d ago GAP-Prompt: Gated Adaptive Prompting for Efficient Continual Learning arXiv:2608.23782v1 Announce Type: new Abstract: Continual learning faces the persistent challenge of catastrophic forgetting, where sequential task updates degrade previously acquired knowledge. While prompt-based methods integrated with pre-trained models offer a compelling… 15 arXiv — Machine Learning research 4d ago AQLoRA: A Zero-Search Recipe for Fast Quantized LoRA Fine-Tuning arXiv:2608.23816v1 Announce Type: new Abstract: Quantized fine-tuning (QLoRA) saves memory but not time. It dequantizes every 4-bit weight on the fly, so it trains more slowly than fp16 LoRA. We present AQLoRA (Adaptive-Quantization LoRA), a recipe that buys part of that time… 10 arXiv — Machine Learning research 4d ago Beyond Static Interpretability: Anticipating Post-SFT Mechanisms from Pre-SFT Parameters for Better Tuning arXiv:2608.24482v1 Announce Type: new Abstract: Mechanistic Localization bridges mechanistic interpretability and post-training optimization by isolating critical parameters via interpretative approaches and then guiding parameter-efficient Supervised Fine-Tuning (SFT) in a… 38 arXiv — Machine Learning research 4d ago Taming foundation model with invariance-oriented pre-training for broad-spectrum EEG analysis across signal-level, brain-state, and brain-health tasks arXiv:2608.24597v1 Announce Type: new Abstract: Electroencephalography (EEG) is a widely used window into human brain function, but most EEG models remain tied to a one-dataset-one-model supervised paradigm. Recent EEG foundation models offer a route toward reusable… 16 arXiv — Machine Learning research 4d ago Parameter-Efficient Self-Supervised Adaptation for EEG-FM under Fixed Computational Budgets arXiv:2608.24727v1 Announce Type: new Abstract: EEG foundation models pretrained via self-supervised learning promise transferable representations, but their generalization remains limited, especially across diverse clinical datasets. Full fine-tuning is impractical for… 10 arXiv — Machine Learning research 4d ago Scaling Reinforcement Learning for Diffusion Models via Velocity Matching arXiv:2608.23664v1 Announce Type: cross Abstract: Reward fine-tuning is becoming an important tool for adapting diffusion models to human preferences and task-specific objectives, but existing methods largely inherit policy-gradient machinery from large language models. Unlike… 17 r/LocalLLaMA community 4d ago me to the model I spent all weekend fine-tuning I just can't resist   submitted by   /u/close_Meal6005 [link]   [comments] 27 arXiv — NLP / Computation & Language research 5d ago Noise Floor Audit for Agent Benchmarks arXiv:2608.22331v1 Announce Type: new Abstract: We audit measurement variability for 3 native tool-calling endpoints across 2 providers on the official BFCL multiple and parallel categories, using matched AST grading. At temperature 0, reruns are nearly deterministic across Groq… 16 arXiv — NLP / Computation & Language research 5d ago HelaBERT: Enhancing Sinhala Language Understanding with Dual Pooling Classification Head arXiv:2608.22922v1 Announce Type: new Abstract: We present HelaBERT, a family of two BERT-based masked language models pre-trained from scratch on approximately 1 billion tokens of Sinhala text sourced from MADLAD-400, CulturaX, and a custom corpus comprising news articles,… 20 Hugging Face Daily Papers research 5d ago TLive-Omni: An Omni-Modal Understanding Model for E-Commerce Live Streaming Abstract TLive-Omni is an omni-modal model for live-commerce that unifies image, video, audio, and text via timestamped token grouping, staged supervised training, and reinforcement fine-tuning with verifiable feedback to enable accurate real-time understanding. Generated by… 24 arXiv — Machine Learning research 6d ago Nothing Changed but the Model: CellFill -- Bounded In-Cell Learning for Bit-Identical, Revocable Updates to Quantized LLMs arXiv:2608.20873v1 Announce Type: new Abstract: Every way of teaching a deployed language model something new -- full fine-tuning, adapter merging, model editing -- replaces the released checkpoint, and with it every evaluation and cache that referred to those exact bits. We… 13 arXiv — Machine Learning research 6d ago Thermo-FL: Thermal-Aware Robust Federated Fine-Tuning of Large Language Models for Edge AI arXiv:2608.21172v1 Announce Type: new Abstract: Federated fine-tuning enables large language models to adapt on edge devices without centralizing private data, but practical deployments must address hardware instability and adversarial update corruption together. Thermally… 4 arXiv — NLP / Computation & Language research 6d ago VA-DPO: Valence-Arousal Direct Preference Optimization for Controllable Emotion Generation in Language Models arXiv:2608.20374v1 Announce Type: new Abstract: How precisely can we tell a language model how to feel? Most work on emotional generation answers with a discrete label - happy, angry, sad - which cannot express a target like "mildly downcast but calm." We instead specify the… 17 arXiv — NLP / Computation & Language research 6d ago Temporal Validity on Real Software Histories: Eliminating Stale-Fact Errors in Code-Assistant Memory over GitHub Fixes arXiv:2608.20685v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) has no model of time: when a fact changes across a coding session - a function is renamed, an endpoint moves, a dependency is bumped - RAG retrieves both the old and new value with… 17 arXiv — Machine Learning research 6d ago Fine-tuning LLMs for Tourist Trajectory Prediction using Field Experiment Data arXiv:2608.20830v1 Announce Type: cross Abstract: Evaluating mobility interventions at tourist destinations requires predicting visitor behavior under varying conditions. Traditional methods struggle because tourist decisions depend heavily on context like weather and fatigue,… 18 arXiv — NLP / Computation & Language research 6d ago A Factorial Ablation of a Speech-to-SFT Pipeline: Differential Effects on Data Quality and Downstream Transfer arXiv:2608.20394v1 Announce Type: cross Abstract: Industry pipelines that turn speech into supervised fine-tuning (SFT) data via multi-stage refinement are increasingly adopted but, to our knowledge, have not been publicly ablated stage-by-stage, leaving each stage's marginal… 8 r/LocalLLaMA community 6d ago 1/100 → 44/100: fine-tuning a 450M VLM on 50K browser screenshots   submitted by   /u/ButtercupLyn100 [link]   [comments] 12 r/LocalLLaMA community 8d ago I feel like I finally graduated. I finally made the move from LM Studio to vLLM thanks to this post https://www.reddit.com/r/LocalLLaMA/s/NmS9CgHvqz . I may not know what it all means yet but I’m going to start diving into the docs to learn as much as I can. I’m running an endpoint on each of my 3090s one for… 4 r/LocalLLaMA community 8d ago Strix Halo (8060S / gfx1151), Qwen-3.8-27B @ Q8 and Q6 UD v3, up to 256K ctx, llama.cpp, DFlash2, vision, real workloads quality and steady performances, optimized recipes, ... Hi fellows fully-local halos, after manually following existing guides, I decided to build an LLM API endpoint installation and optimization guide that works even when autonomously followed by my pi agent, so I can install/experiment/reinstall easily and without babysitting. Q8… 38 TechCrunch — AI news-outlet 8d ago Nvidia just showed that the harness, not the AI model, is now the real hero Nvidia research shows that AI agents can perform well, and not go off the deep end, through fine-tuning, even if the AI model isn't that great at the task. 28 Hugging Face Daily Papers research 8d ago Thinking in a Low-Resource Language: What SFT Builds, What RL Fixes, What Accuracy Cannot See Abstract Fine-tuning large mixture-of-experts models on a low-resource language shifts reasoning into that language without harming accuracy, while reinforcement learning with verifiable rewards fixes formatting and leakage defects. Generated by thinkingmachines/Inkling-Small… 11 Hugging Face Daily Papers research 9d ago EXIMO: VLM Guided Exploration of VLA Policies Abstract EXIMO efficiently fine-tunes large vision-language-action robot policies by combining VLM-guided exploration, imitation on orchestrated data, and residual off-policy reinforcement learning. Generated by thinkingmachines/Inkling-Small How to efficiently finetune robot… 6 arXiv — Machine Learning research 9d ago FAR-DPO: Feasibility-Aware and Robust Direct Preference Optimization for Cyclic Peptide Design arXiv:2608.19808v1 Announce Type: new Abstract: Cyclic peptides are emerging as promising molecular scaffolds in drug discovery due to their high binding affinity and structural stability. However, extending generative models from linear to cyclic peptide design remains… 8 arXiv — Machine Learning research 9d ago Clustering and Token Denoising for Faster and More Robust VLMs arXiv:2608.19285v1 Announce Type: cross Abstract: Recent Visual-Language Models (VLMs) have enhanced the capabilities of pre-trained LLMs by adding vision tokens alongside text, with approaches like LLaVA showing impressive results. However, the computational burden of… 29 arXiv — Machine Learning research 9d ago Fine-Tuning VLAs with Self-Demonstrated Generative Control for Multi-Task Manipulation arXiv:2608.19490v1 Announce Type: cross Abstract: State-of-the-art vision-language-action (VLA) models such as $\pi_{0.5}$ exhibit strong semantic understanding, instruction following and task behavior. However, when deployed on new robots, even minor mismatches in hardware… 5 arXiv — Machine Learning research 9d ago Scaffolding Minds: Optimizing Latent Visual Target Representations for Multimodal Reasoning arXiv:2608.19669v1 Announce Type: cross Abstract: Latent reasoning has advanced multimodal reasoning through a two-stage training paradigm: (1) a helper image is encoded into latent tokens to teach visual chain-of-thought during a supervised fine-tuning (SFT) stage, and (2)… 24 arXiv — NLP / Computation & Language research 9d ago Projector Is All You Train arXiv:2608.19726v1 Announce Type: new Abstract: The typical training process of a multimodal large language model (MLLM) involves adapting both the language model backbone and the projector between the backbone and a modality-specific encoder. We ask whether fine-tuning the… 9 arXiv — NLP / Computation & Language research 9d ago A Speech Corpus for Mizo Automatic Speech Recognition: Whisper and SraVaani 1.0 Fine-Tuning with Morphology-Aware Evaluation arXiv:2608.19361v1 Announce Type: new Abstract: This study reports the development of an Automatic Speech Recognition (ASR) system in Mizo, a low-resource language. The development included collecting 17.62 hours of speech data, curating it, and fine-tuning the Mizo ASR system… 4 Page 1 of 10 · 500 articles Older →
