News / #training Tag Training 500 articles archived under #training · RSS Sign in to follow Hugging Face Daily Papers research 1mo 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 1mo 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 1mo 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 1mo 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 1mo 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 1mo 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 1mo 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 arXiv — NLP / Computation & Language research 1mo ago LoRA-GA$^2$: Low Rank Adaptation with Multi-step Gradient Adaptive Alignment arXiv:2608.19800v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) is a prominent fine-tuning method for large models, achieving competitive performance with reduced memory overhead. However, a persistent performance gap remains between LoRA and full fine-tuning. Recent… 11 arXiv — NLP / Computation & Language research 1mo ago Learning how to Forget: Fine-tuning for Long-Context Sparse Attention arXiv:2608.19920v1 Announce Type: new Abstract: A lot of prior work addressed key-value (KV) cache selection and compression by sparse attention to enable long-context inference for transformer language models without excessive hardware budgets. We provide a new method for… 18 arXiv — NLP / Computation & Language research 1mo ago PEA-DPO: Perception-Enhanced Alignment Direct Preference Optimization for MLLMs Alignment arXiv:2608.19598v1 Announce Type: cross Abstract: Direct Preference Optimization (DPO) has emerged as an effective approach for aligning large language models (LLMs) with human preferences. However, its adaptation to multimodal settings remains unexplored. Through… 9 arXiv — NLP / Computation & Language research 1mo ago DiverValue-Bench: A Benchmark and Fine-Tuning Framework for Aligning Large Language Models with Diverse Human Values arXiv:2509.08022v3 Announce Type: replace Abstract: Aligning large language models (LLMs) with diverse human values is essential for safe and effective deployment, yet existing benchmarks often overlook cultural and demographic variation. We introduce DiverValue-Bench, a… 13 Hugging Face official-blog 1mo ago How Hugging Face Inference Endpoints, Jobs, and Buckets Power Search on Papers with Code Back to Articles a]:hidden"> How Hugging Face Inference Endpoints, Jobs, and Buckets Power Search on Papers with Code Published August 21, 2026 Update on GitHub Upvote 1 Niels Rogge nielsr :last-child]:mb-0"> 3 months ago, we started a revival of Papers with Code (see also the… 35 r/LocalLLaMA community 1mo ago Fine-tuning Cactus Needle 2 can match DeepSeek v4 on the specific task Hey LocalLlama, Henry from Cactus here! When we trained Needle 2, I had a strict rule to not expose the model to any data sample that remotely felt like these benchmarks. It seemed over-the-top, but benchmarks are easy to overfit around, yet struggle in the wild, especially… 9 r/LocalLLaMA community 1mo ago Ling-3.0 released all 6 base checkpoints: 2 sizes × 3 stages AntLing has released the full six-checkpoint matrix for the Ling-3.0 base model. tiny: pretrained, mid-trained, WSM-merged flash: pretrained, mid-trained, WSM-merged The concrete artifact is six separate official repositories, not one endpoint repeated under different names. All… 32 r/LocalLLaMA community 1mo ago I just built a mini Kimi-K3 from Scratch under 250$. Already beats GPT-2 (124M)! I pre-trained a 1.02-billion-parameter on Kimi K3 replica trained on 5.00 billion decontaminated tokens for $250. This model has 1.02 billion parameters, of which 145 million are active per token. It is roughly one two-thousandth of K3 by total size. It saw 5,000,003,584 tokens,… 12 arXiv — Machine Learning research 1mo ago Off-Manifold Collapse in Guided Protein Language Models arXiv:2608.18597v1 Announce Type: new Abstract: Protein language models are widely used priors for protein sequence design, and a growing body of work controls them at inference time as an alternative to fine-tuning. Such guidance faces a dilemma: mild enough to preserve natural… 20 arXiv — Machine Learning research 1mo ago Bridge Graphical Models: Coupling, Projection, and Current-Preserving Dynamics for Generative Modeling arXiv:2608.19144v1 Announce Type: new Abstract: Continuous-time generative models are often built from endpoint-conditioned bridges, but generation requires a different object: a non-anticipative Markov decoder that only observes the current state and time. We identify this… 16 arXiv — Machine Learning research 1mo ago Learned, Then Lost: A Measured Single-Example Counterfactual in Pre-training arXiv:2608.19168v1 Announce Type: new Abstract: A single training example's contribution to a finished model is normally estimated rather than measured, because measuring it takes two expensive full pre-training runs that differ in one row of one batch. We ran that… 21 arXiv — NLP / Computation & Language research 1mo ago Alignment Is All You Need: Instruction-Free Training for General Audio-Language Models arXiv:2608.18132v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are typically built through a multi-stage pipeline consisting of cross-modal alignment, supervised fine-tuning (SFT), and preference optimization. This pipeline assumes that adapting an LLM… 30 arXiv — NLP / Computation & Language research 1mo ago Figurative and Cultural Knowledge in LLMs: Investigating Cross-Domain Transfer through Fine-Tuning arXiv:2608.18361v1 Announce Type: new Abstract: Figurative language is deeply culturally embedded; fluent use requires not just linguistic competence but cultural immersion. We ask whether LLMs can learn this link: does fine-tuning on cultural data improve figurative language… 25 r/LocalLLaMA community 1mo ago Qwen3.8-23B-Mini-Me: A Depth-Pruned Qwen3.8-27B (to ~22.7BB) I've been working on a depth pruning approach and decided to try it out on the new Qwen3.8-27B model. I managed to get the model down to about 22.7B params without severe reasoning degradation. No fine-tuning was done, just strategic removal of layers. It's been working well for… 13 r/MachineLearning community 1mo ago Same GRPO recipe on three from-scratch LLMs (353M/316M/672M) gave three different outcomes, with no clean relationship to scale [P] I trained three LLMs from scratch in raw PyTorch then post-trained each one with SFT and then GRPO. Same process every time: same synthetic arithmetic curriculum, same reward function, same hyperparameters, same KL coefficient. Pre-training went as expected, the val loss went… 5 r/LocalLLaMA community 1mo ago AntLing’ve open-sourced 6 Base Model checkpoints for Ling-3.0-tiny & Ling-3.0-flash, covering pre-trained, mid-trained, and WSM-merged stages. None has undergone post-training, giving researchers flexible starting points for continued pre-training, fine-tuning, and further research. Two key highlights: - They use WSM to replace LR decay with weighted checkpoint merging, making the training process better suited for… 18 Hugging Face Daily Papers research 1mo ago PTXBench: Benchmark and Adapt LLMs for GPU Kernel Optimization with Architecture-specific PTX Abstract PTXBench evaluates large language models on architecture-specific GPU kernel optimization, revealing uneven success and performance gaps that supervised fine-tuning only partially addresses. Generated by thinkingmachines/Inkling-Small We introduce PTXBench, a benchmark… 27 Hugging Face Daily Papers research 1mo ago PixRestore: Unified Image Restoration via Pixel Diffusion Transformer Abstract PixRestore is a compact, VAE-free pixel-space diffusion transformer trained from scratch for unified image restoration, using flow matching on patchified pixels, DINO-based reliability-guided feature fusion, and adversarial fine-tuning to a one-step generator for… 9 arXiv — Machine Learning research 1mo ago Data-DPO: Direct Preference Optimization for Target Model Data Selection in LLM Post-Training arXiv:2608.16926v1 Announce Type: new Abstract: Data selection in supervised fine-tuning aims to select a small set of effective samples from large-scale candidate data, reducing training cost while preserving model performance. However, existing methods usually treat data value… 15 arXiv — Machine Learning research 1mo ago Hierarchical Data Selection via Manifold Coverage and Sparse Feature Coverage in LLM Post-training arXiv:2608.16927v1 Announce Type: new Abstract: As supervised fine-tuning data continues to scale, selecting high-value subsets from large candidate pools is crucial for reducing training cost and improving model performance. Existing methods often measure diversity directly in… 17 arXiv — Machine Learning research 1mo ago OraclePhys: A Systematic Framework for LLM Fine-Tuning on Structural Mechanics arXiv:2608.17162v1 Announce Type: new Abstract: What a language model internalizes from fine-tuning is usually diagnosed after the fact. We make it an experimental variable. OraclePhys is a systematic fine-tuning framework with three components: OraclePhys-Bench, an… 30 arXiv — Machine Learning research 1mo ago Q-Learning With World Models arXiv:2608.17163v1 Announce Type: new Abstract: Off-policy reinforcement learning (RL) has become increasingly sample-efficient, enabling applications such as RL fine-tuning of Vision-Language-Action models into reliable, high-performing policies. World models offer a further… 9 arXiv — Machine Learning research 1mo ago Task Specialization Fine-Tuning for Contextual Reinforcement Learning arXiv:2608.17180v1 Announce Type: new Abstract: Contextual Reinforcement Learning (CRL) seeks to generalize classical RL by maximizing task coverage across a context space of related tasks. While prior works often train from scratch and rely on either multi-task learning for a… 38 arXiv — Machine Learning research 1mo ago Agentic ESOpt: Fine-Tuning Long-Horizon LLM Agents with Minimal GPU Requirements arXiv:2608.17310v1 Announce Type: new Abstract: Reinforcement Learning (RL) has been promising in single-turn LLM fine-tuning. However, long-horizon agentic reasoning introduces increasingly branching interactions and sparse rewards, exposing several limitations of RL: its… 25 arXiv — Machine Learning research 1mo ago CORAM: Coherent Orthogonal Rotation for Model Merging arXiv:2608.17366v1 Announce Type: new Abstract: Merging finetuned models combines specialized capabilities without joint training or access to the original data. Most methods operate by linear arithmetic in Euclidean weight space, which cannot carry the geometry of the update.… 36 arXiv — Machine Learning research 1mo ago Debate Training Reduces Reward Hacking in RLAIF arXiv:2608.17776v1 Announce Type: new Abstract: We demonstrate that RL finetuning an LLM using debate, a two-player adversarial game between a generator and a critic adjudicated by a weaker LLM judge, reduces reward hacking compared to a reinforcement learning from AI feedback… 8 arXiv — Machine Learning research 1mo ago VLCP: Vision Language Control Policy Closed-Loop Code Replanning for Robot Manipulation arXiv:2608.16978v1 Announce Type: cross Abstract: Turning a frontier vision-language model into a robot policy usually means fine-tuning it to emit an action representation it never saw in pretraining, which throws away much of the reasoning that made the model worth reaching… 13 arXiv — NLP / Computation & Language research 1mo ago How Do Large Language Models Learn Concepts During Continual Pre-Training? arXiv:2601.03570v2 Announce Type: replace Abstract: Human beings primarily understand the world through concepts (e.g., dog), abstract mental representations that structure perception, reasoning, and learning. However, how large language models (LLMs) acquire, retain, and forget… 36 arXiv — NLP / Computation & Language research 1mo ago Parametric Knowledge in RAG-SFT for Domain-Specific Document Generation arXiv:2603.23047v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) fine-tuning has shown substantial improvements over vanilla RAG, yet most studies target document question answering, leaving open whether these gains transfer to specialized tasks. We study… 19 Hugging Face Daily Papers research 1mo ago Energy-Guided Flow Matching Abstract Energy-Guided Flow Matching improves generative quality by progressively revealing high-frequency details through a moving endpoint and adaptive scheduling, reducing training cost and achieving state-of-the-art FID scores. Generated by thinkingmachines/Inkling-Small… 7 Hugging Face Daily Papers research 1mo ago Agentic ESOpt: Fine-Tuning Long-Horizon LLM Agents with Minimal GPU Requirements Abstract Agentic ESOpt uses evolution strategies for scalable full-parameter fine-tuning of long-horizon LLM agents via trajectory-level reward-weighted updates and parameter-context co-evolution. Generated by thinkingmachines/Inkling-Small Reinforcement Learning (RL) has been… 12 OpenAI Python SDK releases dev-tools 1mo ago v3.3.0 3.3.0 (2026-08-18) Features support named data-residency endpoints ( #3646 ) ( 11ee914 ) Bug Fixes require patched optional networking dependencies ( #3651 ) ( 40e56de ) Chores remove unused dependencies and pin build tooling ( #3650 ) ( eee8e4a ) Documentation strengthen Python… 22 Hugging Face Daily Papers research 1mo ago AnyTalk: Speech Animation for Arbitrary Characters Leveraging a Video Generation Model Abstract AnyTalk generates 3D speech animations for arbitrary characters without animation data by adapting video diffusion models via character-specific fine-tuning and optimizing blendshape parameters from synthesized talking-head videos, with a distilled real-time variant.… 9 arXiv — Machine Learning research 1mo ago Forward Pass Domain Adaptation (Without Cross-Layer Backpropagation) arXiv:2608.14563v1 Announce Type: new Abstract: Forward-Pass-Only MLP training (FPO) adapts large language models without a backward pass through the model body, achieving 2.7--3.2x the throughput of standard fine-tuning at ~40% less peak training memory, while leaving… 16 arXiv — Machine Learning research 1mo ago SAPE: Sandwich Adapters for Parameter Efficiency in Large Language Model Fine-Tuning arXiv:2608.15360v1 Announce Type: new Abstract: While Parameter-Efficient Fine-Tuning (PEFT) has substantially reduced the hardware cost of adapting Large Language Models (LLMs) by decreasing the number of trainable parameters, recent studies have sought to further improve PEFT… 19 arXiv — Machine Learning research 1mo ago Towards a theory of inference-time alignment with unknown rewards arXiv:2608.15402v1 Announce Type: new Abstract: Generative model alignment has received broad interest, and significant progress has been made in supervised fine-tuning and inference-time computation. Yet, alignment has remained poorly understood from a statistical learning… 23 arXiv — Machine Learning research 1mo ago UniFed-VLM: Federated Instruction Tuning for Vision-Language Models with Multiple Heterogeneity arXiv:2608.15516v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have demonstrated strong performance in multimodal understanding and generation. However, fine-tuning of VLMs typically relies on centralized data, which raises privacy concerns in certain domains… 32 arXiv — NLP / Computation & Language research 1mo ago Why Summaries Turn Neutral: Policy Attribution for Sentiment Drift in Reinforcement Learning from Human Feedback arXiv:2608.15530v1 Announce Type: new Abstract: Reinforcement learning with human feedback (RLHF) aligns LLMs with human preferences, improving summarization fluency and safety, but causes sentiment drift: overly neutral summaries stripped of emotional nuance. We diagnose why RL… 12 arXiv — NLP / Computation & Language research 1mo ago Token Distribution versus Data Volume: Domain Balancing in Multi-Domain Meeting Summarisation arXiv:2608.15935v1 Announce Type: new Abstract: Jointly fine-tuning an LLM on meeting-summarisation corpora of widely varying size raises a question that prior work leaves confounded: when a domain-balanced training mixture helps, is the gain due to the distribution of tokens… 36 arXiv — NLP / Computation & Language research 1mo ago Domain-Agnostic Neural Topic Modeling with Contextual Token-Level Semantic Graph Representation arXiv:2608.16269v1 Announce Type: new Abstract: Recent advances in neural topic models with pre-trained language models (PLMs) have achieved strong performance by leveraging general-domain pre-training, yet their topic interpretability often degrades on specialized corpora. This… 4 arXiv — NLP / Computation & Language research 1mo ago Step-Level On-Policy Distillation: Interpolating Between On-Policy Distillation and Supervised Fine-Tuning arXiv:2608.16333v1 Announce Type: new Abstract: On-policy distillation (OPD) aligns a student model with a teacher's logit distribution on student-generated trajectories. This approach has achieved strong empirical gains and can often surpass conventional off-policy distillation… 37 arXiv — NLP / Computation & Language research 1mo ago Palmyra x6 Technical Report: An Agentic, Tool-Use Model Post-Trained via Anchored Supervised Fine-Tuning arXiv:2608.16620v1 Announce Type: new Abstract: Palmyra x6 is a large language model optimized for use with enterprise-oriented agentic tasks. The model was built by post-training a Mixture-of-Experts base model with Anchored Supervised Fine-Tuning on a compact corpus of… 32 OpenAI Python SDK releases dev-tools 1mo ago v3.2.0 3.2.0 (2026-08-17) Features add Bedrock Runtime endpoint support (SDK-290) ( #3623 ) ( 86267d2 ) api: Add shell call streaming events and new service/image types ( #3635 ) ( ff14a33 ) 6 Page 7 of 10 · 500 articles ← Newer Older →
