News / #training Tag Training 500 articles archived under #training · RSS Sign in to follow r/LocalLLaMA community 4h ago Using GPT Astra to teach Qwen Next how to sculpt in 3D in Blender. Instead of distillation, fine-tuning, etc., I found a faster approach: having Codex+Astra to teach OpenCode + MCP Blender to make the modifications. Goal: achieve the same result using specific skills and steps. Astra is phenomenal, just as phenomenal as it is at quickly burning… 34 Anthropic SDK (Python) releases dev-tools 1d ago v1.4.0 1.4.0 (2026-09-04) Full Changelog: v1.3.0...v1.4.0 Features api: add Claude Tag category and user breakdowns to usage reports ( 9fce1e4 ) api: add named types for organization compliance settings state ( 1de1957 ) api: add support for sending a workspace ID on more endpoints (… 32 The Information — AI news-outlet 2d ago Saudi Firm Humain Unveils Arabic Language Model Developed With China’s MiniMax Humain, Saudi Arabia’s state-owned AI company, announced on Thursday an Arabic large language model developed based on Chinese AI firm MiniMax’s model. The humain-m3 model, built on MiniMax’s M3 open-source model, was pre-trained on more than 1 trillion tokens of Arabic content,… 30 Hugging Face Daily Papers research 2d ago Terminal-Universe: Turning Agent Trajectories into Scalable Terminal Environments Abstract Terminal-Universe reconstructs executable workspaces from agent trajectories to synthesize diverse training tasks and improves post-training performance through supervised fine-tuning. Generated by thinkingmachines/Inkling-Small As terminal-based code agents become… 9 arXiv — NLP / Computation & Language research 2d ago Routing Is Not Enough: Diagnosing Intra-Adapter Subspace Contention in MoE+LoRA Fine-Tuning arXiv:2609.03150v1 Announce Type: cross Abstract: Multi-domain fine-tuning often combines MoE routing with LoRA, assuming that token-level routing separates domain-specific updates. We test this assumption in MoE+LoRA using Python code paired with biomedical text and… 23 arXiv — Machine Learning research 2d ago Extracting Forgotten Prompts from Targeted Unlearned Models arXiv:2609.03662v1 Announce Type: new Abstract: Recent unlearning methods (e.g. NPO, DPO, LUNAR) make use of refusal alignment to suppress forgotten data. However, it has been shown that refusal responses might leave traces of unlearning, and recent attacks have been able to… 11 arXiv — Machine Learning research 2d ago Beyond Endpoint Scores: Time- and Capacity-Conditioned Evaluation of Continual Knowledge Updating arXiv:2609.03900v1 Announce Type: new Abstract: Continual knowledge-updating methods are often declared superior from one final checkpoint and one conventional adapter rank. We show that this can be insufficient to identify the better operating point. Holding a periodic… 27 arXiv — Machine Learning research 2d ago Subspace Inference Enables Efficient Active Reward Learning from Preferences arXiv:2609.04066v1 Announce Type: new Abstract: Reinforcement learning from human feedback (RLHF) has emerged as a powerful yet sample-inefficient approach for learning reward models from human preferences, making active learning a critical component in synthesizing informative… 19 arXiv — NLP / Computation & Language research 2d ago Beyond Shallow Alignment: How Post-Training Methods Determine Refusal Circuits And Steering Robustness arXiv:2609.03887v1 Announce Type: new Abstract: How do the methods used to train language models to refuse harmful requests shape how that refusal actually works inside the model? We compare three post-training methods - supervised fine-tuning, reasoning-augmented fine-tuning… 8 arXiv — NLP / Computation & Language research 2d ago Knowledge Acquisition During Pre-training? Large Language Models Learn Better With Auxiliary Views arXiv:2609.04180v1 Announce Type: new Abstract: Gaps remain in our understanding of how large language models (LLMs) acquire knowledge during pre-training. We posit that auxiliary views, reformulations of knowledge, are causally helpful for learning. We design controlled… 30 arXiv — NLP / Computation & Language research 2d ago ALRA: Adaptive Local Relational Alignment for Logit-Based Pre-training Distillation of Autoregressive Language Models arXiv:2609.03355v1 Announce Type: cross Abstract: Logit-based knowledge distillation for autoregressive language models usually aligns teacher and student next-token distributions over the entire vocabulary. However, this global objective overlooks relative preferences among… 13 Hugging Face Daily Papers research 2d ago LLaDA-Image: Building Strong Image Generators with Fully Open Training Recipes Abstract LLaDA-Image unifies a 6B diffusion transformer with a frozen vision-language module, using image-only pre-training and a Muon optimizer to generate photorealistic images with precise editing, and is distilled into a fast 2-4 step variant that achieves state-of-the-art… 26 r/LocalLLaMA community 2d ago Increasing active parameters per token in MOE (Qwen 35B A4B+) reduce reasoning token by 8.5% - and you don't need to train or finetune! I want to share a short paper just published exploring a simple but surprisingly effective optimization for sparse MoE reasoning models. The idea: Instead of retraining anything, we just tweak the router at runtime . Specifically, we expand the expert selection budget (N≥K N ≥ K… 24 r/LocalLLaMA community 3d ago I built a local web UI to finetune models on my own text and actually watch the training (works on AMD ROCm) I wanted to do continued pretraining/finetuning of a local model on my own notes and see what's happening while it trains and do it on my AMD card, since most tools assume CUDA. llm-training-panel is a local web UI that: - loads a model from a local dir or a HF id - shows a live… 34 Hugging Face Daily Papers research 3d ago NeoMME: A Single-Tower Multimodal-Native Multilingual Foundation Encoder for Efficient Fine-Tuning and Inference Abstract NeoMME introduces small bidirectional multimodal encoders pretrained with masked discrete diffusion that achieve strong visual document retrieval and high compression of late-interaction embeddings. Generated by thinkingmachines/Inkling-Small Multimodal models often… 21 Hugging Face Daily Papers research 3d ago Post-Training Language Models for Gold-Medal Performance in Coding Competitions Abstract A specialization pipeline combining curated problems, synthetic reasoning, supervised fine-tuning, and reinforcement learning trains competitive programming models that exceed top human scores on IOI benchmarks using iterative test-time refinement. Generated by… 16 arXiv — Machine Learning research 3d ago Rethinking the Teacher-Student Framework for Test-Time Adaptation arXiv:2609.02507v1 Announce Type: new Abstract: Test-Time Adaptation (TTA) has recently emerged as a promising strategy that allows the adaptation of pre-trained models to changing data distributions at deployment time, without access to any labels. To mitigate error… 29 arXiv — Machine Learning research 3d ago Hearing the Whispers: Black-Box Membership Inference Attacks on Finetuned TTS Models arXiv:2609.01723v1 Announce Type: cross Abstract: Text-to-Speech (TTS) foundation models are increasingly fine-tuned on private datasets to synthesize highly personalized voices, introducing severe privacy risks by exposing both biometric identities and sensitive speech content.… 36 arXiv — NLP / Computation & Language research 3d ago SpeakPay: Domain-Adaptive LoRA Fine-Tuning of Whisper for Low-Resource Nepali Financial Speech Recognition arXiv:2609.01737v1 Announce Type: new Abstract: Mobile payment applications in Nepal are graphically mediated and largely inaccessible to visually impaired users. This paper presents SpeakPay, a voice-first digital wallet, and documents the central technical contribution: a… 26 arXiv — NLP / Computation & Language research 3d ago NS-Copilot: An LLM-Driven Agent System for Autonomous Neuroscience Analysis arXiv:2609.01971v1 Announce Type: new Abstract: AI is rapidly advancing neuroscience, yet many laboratories fail to fully unleash its potential due to significant interdisciplinary barriers. While pre-trained neural models for physiological data are progressing quickly, their… 29 arXiv — NLP / Computation & Language research 3d ago IDEEA: training-free Input-Dependent stEEring via Activation cluster matching arXiv:2609.02089v1 Announce Type: new Abstract: Steering aligns large language models (LLMs) by injecting a bias into selected activations at inference time, offering a far cheaper alternative to weight-update methods such as supervised fine-tuning or reinforcement learning.… 35 arXiv — NLP / Computation & Language research 3d ago TaRA: Training-Aware Low-Rank Adaptation Initialization arXiv:2609.02639v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) has become a de facto standard for parameter-efficient fine-tuning (PEFT), yet its performance is highly sensitive to initialization due to the information bottleneck imposed by low-rank decomposition.… 33 arXiv — NLP / Computation & Language research 3d ago Choosing a PEFT Variant for Per-Patient Dysarthric ASR: A Single-Speaker Case Study on Two ASR Bases arXiv:2609.02735v1 Announce Type: new Abstract: Per-patient adapters are the preferred production architecture for dysarthric automatic speech recognition (ASR), yet parameter-efficient fine-tuning (PEFT) variants have not been compared in the speaker-dependent, per-patient… 7 llama.cpp releases dev-tools 3d ago b10774 finetune: fix no KV cache ( #27199 ) training: fix no KV cache apply @ ggerganov suggestion Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/44871971 macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled)… 27 Hugging Face official-blog 3d ago Fine-tuning a 350M Model for Better Structured Outputs in 100 GRPO Steps Back to Articles a]:hidden">Fine-tuning a 350M Model for Better Structured Outputs in 100 GRPO Steps Published September 3, 2026 Update on GitHub Upvote - Leonie Monigatti iamleonie LiquidAI ben burtenshaw burtenshaw Sergio Paniego sergiopaniego :last-child]:mb-0"> This guide is… 8 arXiv — Machine Learning research 4d ago Task-Specific Prompt with Global Context for Multi-Task Graph Pre-Training arXiv:2609.00047v1 Announce Type: new Abstract: Graph prompt learning is an effective paradigm to adapt pre-trained graph models to downstream tasks in low-resource scenarios. However, existing multi-task graph pre-training frameworks generally use randomly initialized prompts,… 7 arXiv — Machine Learning research 4d ago Attention Sensitivity Is Not Enough: Dissociating Attention-Level and Behavioural In-Context Learning under Fine-Tuning arXiv:2609.00064v1 Announce Type: new Abstract: In-context learning (ICL) lets large language models adapt to new tasks from demonstrations, and fine-tuning can erode this behaviour. Many preservation diagnostics inspect attention: if attention changes when demonstrations… 31 arXiv — Machine Learning research 4d ago RW-LoRA: Communication-Efficient Decentralized LoRA Fine-Tuning via Random Walks arXiv:2609.00078v1 Announce Type: new Abstract: Parameter-efficient fine-tuning methods such as LoRA have become a standard approach for adapting large foundation models. Adopting fine-tuning to distributed settings faces several challenges. Most existing distributed LoRA… 37 arXiv — Machine Learning research 4d ago Elite-Weighted Supervised Fine-tuning for Goal-Directed Molecular Optimization arXiv:2609.00189v1 Announce Type: new Abstract: Goal-directed optimization is essential for steering molecular generators to propose candidates with desired properties. However, it is often implemented with policy-gradient reinforcement learning, which requires a… 30 arXiv — Machine Learning research 4d ago CRAFT: Fine-Tuning Pre-hoc Explainability in AI-native 6G RAN arXiv:2609.00590v1 Announce Type: new Abstract: The next generation of mobile networks is envisioned as fully AI-native, with AI-RAN architectures embedding small language models (SLMs) to perform reasoning over real-time telemetry. The state-of-the-art training paradigms for… 26 arXiv — Machine Learning research 4d ago Breaking the Structural Identity: Personalized Federated LoRA Fine-tuning under Rank Heterogeneity arXiv:2609.00632v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable success across diverse domains, but their adaptation to privacy-sensitive, distributed datasets remains a challenge. While Federated Learning (FL) combined with Low-Rank… 5 arXiv — Machine Learning research 4d ago Online Self-Weighted Fine-Tuning arXiv:2609.00734v1 Announce Type: new Abstract: Standard supervised fine-tuning (SFT) assigns the same explicit loss weight to every expert demonstration, regardless of the model's changing competence over training queries. Reinforcement learning (RL) based methods adapt update… 14 arXiv — Machine Learning research 4d ago Text Capability Loss in Vision-Language Adaptation: An Attention-Sink Diagnosis arXiv:2609.00746v1 Announce Type: new Abstract: Fine-tuning a pretrained LLM into a vision-language model (VLM) can erode the backbone's text capability, with the damage concentrated on tasks that require following exact output rules, such as instruction following,… 17 arXiv — Machine Learning research 4d ago Frozen Cores Need Task Signal: Fisher-Whitened Cross-Covariance for Low-Resource LLM Adaptation arXiv:2609.00762v1 Announce Type: new Abstract: Parameter-efficient fine-tuning is usually framed as a question of how many parameters to update. Under a severe trainable-state budget, however, where those coefficients act is equally consequential. We study this choice through… 31 arXiv — Machine Learning research 4d ago Scaled Idempotence in Transformer Attention: Paired OV Geometry and Shared-Value Algebras arXiv:2609.01129v1 Announce Type: new Abstract: We identify a recurrent algebraic regularity in Transformer attention: a sparse subset of effective OV operators $T=OV^\top$ nearly closes under composition, $T^2\approx\alpha T$. Across six pretrained endpoints spanning 2.8B--235B… 27 arXiv — Machine Learning research 4d ago Post-Training Science for Supervised Fine-Tuning arXiv:2609.01244v1 Announce Type: new Abstract: Every supervised fine-tuning run forces the same chain of decisions, such as learning rate, batch size, LoRA or full fine-tuning, how many epochs, which optimiser, and what data to feed the model. Each of these is typically… 32 arXiv — NLP / Computation & Language research 4d ago ValueGraph: Value-Signal Guided Graph Pre-training for Contextualized User Representation arXiv:2609.00057v1 Announce Type: new Abstract: Value signals are aggregated user-level moral representations that capture users' inferred value-related tendencies from their online discourse. User behavior on social media is shaped not only by what users say or whom they… 17 arXiv — NLP / Computation & Language research 4d ago KItCAT: Knowledge Injection via Input Corruption for Auto-regressive Training arXiv:2609.00082v1 Announce Type: new Abstract: LLMs acquire vast amounts of knowledge during pre-training, but often lack the specialized knowledge needed to answer questions from niche sources such as manuals or technical documents unseen during pre-training. Continued… 11 arXiv — NLP / Computation & Language research 4d ago Uncovering and Mitigating Aggregation-Induced Reward Hacking in Multi-Reward Reinforcement Learning arXiv:2609.00213v1 Announce Type: new Abstract: Reinforcement learning fine-tuning of large language models increasingly adopts multiple reward dimensions, including verifiable rules, task-specific evaluators, and learned reward models, to provide richer supervision across… 30 arXiv — NLP / Computation & Language research 4d ago Two locked tests of phase-structure features for transition prediction arXiv:2609.00335v1 Announce Type: new Abstract: A published theoretical account of phase structure in rotary attention was subjected to two pre-specified empirical tests of whether phase-derived features improve prediction of a commitment or contradiction endpoint over a… 31 arXiv — NLP / Computation & Language research 4d ago Context-Grounding Gains Are Mediated by Pre-existing Machinery: Auditing GRPO, SFT, and DPO arXiv:2609.00925v1 Announce Type: new Abstract: Language models can ignore prompt evidence when it conflicts with memorized knowledge. Post-training can make models follow such evidence more reliably, but it is unclear whether these gains require new machinery or strengthen… 6 Hugging Face Daily Papers research 4d ago ZimaBlue: Evolving Generalizable World Action Models through Scalable Video Pre-training Abstract ZimaBlue learns generalizable world action models from large-scale egocentric video via a three-stage curriculum and a slow-fast architecture, substantially improving zero-shot robotic manipulation. Generated by thinkingmachines/Inkling-Small Robotic manipulation faces… 12 r/MachineLearning community 5d ago I audited 112 real RL post-training environments for reward-hacking vulnerabilities — 54 flagged, 0 false positives [OC, tool] [P] RL post-training (RLHF/RLAIF/GRPO) agents optimize strictly for whatever the verifier rewards. If the verifier has logic flaws, the agent learns to hack the grader instead of solving the task — recent work has catalogued this at scale (Terminal Wrench found 331 hackable… 30 r/LocalLLaMA community 5d ago Multilingual Tiny (3.7B) Reasoning MoE pretrained from scratch on a consumer-grade GPU Hello! I've just uploaded a recent checkpoint of my model trained from scratch: https://huggingface.co/piotr-ai/polanka_3.7b_exp_wip_260901 It was pre-trained, mid-trained, and fine-tuned on a single 4090 over many months. How many tokens? I lost count. Feel free to use it as a… 10 Hugging Face Daily Papers research 5d ago Dynamic Important Example Mining for Reinforcement Finetuning Abstract DIEM adaptively selects and reweights training examples during reinforcement fine-tuning by estimating each sample's marginal contribution to policy improvement and constraining batch updates to stabilize optimization. Generated by thinkingmachines/Inkling-Small… 21 Hugging Face Daily Papers research 5d ago CogEvol: Towards Efficient and Reliable Learning Environment Generation Abstract CogEvol is a family of models that generate structured learning artifacts in a single pass using supervised fine-tuning and reinforcement learning with vision-language rewards, achieving high quality with far fewer parameters and lower cost. Generated by… 6 arXiv — Machine Learning research 5d ago Reference-Grafting Matches Fine-Tuning at Eliciting Sandbagged Capabilities arXiv:2608.29458v1 Announce Type: new Abstract: Sandbagging, in which a model deliberately underperforms on an evaluation despite retaining the underlying capability, threatens the safety evaluations that frontier-model governance depends on. The Elicitation Game found that… 14 arXiv — Machine Learning research 5d ago Reward-guided Fine-Tuning of One-Step Generative Models via Wasserstein Gradient Flow arXiv:2608.29647v1 Announce Type: new Abstract: To mitigate the time complexity of generative models, one-step generative models have recently emerged through direct mapping from noise to data in a single forward pass. However, the reward-guided fine-tuning method of one-step… 13 arXiv — NLP / Computation & Language research 5d ago Cloud and On-Premises Deployment of Uzbek Legal RAG via Targeted Retriever Fine-Tuning arXiv:2608.29284v1 Announce Type: new Abstract: Deploying large language models for legal question answering raises challenges that general-purpose leaderboards do not capture, particularly for low-resource languages and under hard operational constraints. We report on building… 21 arXiv — NLP / Computation & Language research 5d ago When to Adapt: Conditional Memory Adapters for Retention-Preserving Domain Specialization arXiv:2608.29327v1 Announce Type: new Abstract: Large language models deployed in specialized domains must improve in-domain performance without sacrificing general capabilities. Existing parameter-efficient fine-tuning methods are typically always on: their learned… 38 Page 1 of 10 · 500 articles Older →
