🔥 TurboVLA: Real-Time Vision-Language-Action Model at 32 Hz on an RTX 4090 with <1 GB VRAM

💡 The paper introduces TurboVLA, a new vision-language-action model that achieves real-time performance at 32 Hz on an RTX 4090 with less than 1 GB VRAM. The conventional approach to vision-language-action models involves using a large language model as the central interface between perception and action, which incurs substantial computational and memory overhead. In contrast, TurboVLA reformulates the conventional pathway as a direct vision-language-action mapping, where visual observations and language instructions are independently encoded and directly exchanged through lightweight bidirectional vision-language interaction. This simplified design constructs task-conditioned representations directly from visual and linguistic features, significantly reducing computational and memory costs.

The TurboVLA model achieves 97.7 percent average success with only 0.2 billion parameters, 31.2 ms inference latency, and 0.9 GB inference VRAM on a consumer-grade RTX 4090, matching or outperforming substantially larger vision-language-action policies. The results establish TurboVLA as a simple and effective alternative to the prevailing language-centric vision-language-action paradigm, offering a new perspective on how vision, language, and action can be connected for efficient robotic manipulation. The code for TurboVLA is available online, making it accessible for further research and development. Overall, the paper presents a significant contribution to the field of vision-language-action modeling, demonstrating the potential for real-time and efficient robotic manipulation using a novel and simplified approach.

📅 Published on Jul 29

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2607.27205
• PDF: https://arxiv.org/pdf/2607.27205
• Project Page: https://h-embodvis.github.io/TurboVLA/

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📢 By: https://t.iss.one/PaperNexus

#VisionLanguageAction #RealTimeAI #EfficientDeepLearning #VisionLanguageInteraction #LowMemoryComputing

🔥 DistillAlign: Coordinating Mode Covering and Mode Seeking in Autoregressive Video Distillation

💡 This paper addresses the issue of existing autoregressive video distillation methods which typically decouple the initialization and distribution matching stages, leading to suboptimal results. The authors argue that a good initialization should match the mode coverage of the target distribution, rather than merely pursuing high quality. To analyze this, they introduce a distributional evaluation protocol that measures precision and coverage between student and teacher distributions in a shared latent space.

The authors find that some initializations reach high precision but low coverage, leading to suboptimal refinement, while mode-covering ones preserve broader support. Furthermore, even when the target distributions are aligned, the reverse-KL objective of distribution matching can still drive the student towards high-probability teacher regions in late training, reducing coverage and diversity.

To address this, the authors propose joint distillation, which combines the mode-seeking objective of distribution matching with a consistency distillation-based mode-covering constraint. The experiments show that their method improves generation quality, coverage, and diversity. Notably, even with a smaller teacher model, their method outperforms baselines refined with a larger teacher model, underscoring the importance of distributional alignment in autoregressive video distillation.

The main contributions of this paper are the introduction of a distributional evaluation protocol and the proposal of joint distillation, which coordinates mode covering and mode seeking in autoregressive video distillation. The results demonstrate the effectiveness of the proposed method in improving the quality and diversity of generated videos.

📅 Published on Jul 29

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2607.26811
• PDF: https://arxiv.org/pdf/2607.26811
• Project Page: https://lijiaxing0213.github.io/DistillAlign/

📊 Datasets citing this paper:
• https://huggingface.co/datasets/LiJiaxing/DistillAlign_1p3b_25K

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📢 By: https://t.iss.one/PaperNexus

#AutoregressiveVideoDistillation #ModeCovering #DistributionMatching #VideoDistillationMethods #LatentSpaceAnalysis

🔥 Frontis-MA1: Training an AI4AI Model towards Recursive Self-Improvement in Machine Learning Engineering

💡 The paper introduces Frontis-MA1, a model that achieves recursive self-improvement in machine learning engineering. Recursive self-improvement requires AI systems to improve the process of building AI, and machine learning engineering offers a concrete test bed for studying this capability. The authors propose OpenMLE, an open full-stack system for recursive self-improvement research in machine learning engineering, which includes a verifiable task environment with execution feedback, operator learning, and long-horizon search.

The Frontis-MA1 model is trained as a meta-evolution agent for machine learning engineering, aligning post-training and inference around four atomic program-evolution operators: Draft, Improve, Debug, and Crossover. These operators are trained via execution-grounded self-supervised training and reinforcement learning on data duplicated against all evaluation benchmarks, then composed into long-horizon search, coupling learning and evolution in a single loop.

The results show that Frontis-MA1 improves the Medal Average from 39.39% to 60.61% over its base model with OpenMLE-Evo, and reaches 71.21% with OpenMLE-Evo-Max, exceeding GPT-5.5+Codex and approaching GPT-5.6 Soland and the 2.8T Kimi K3. On the held-out Nature Bench Lite, both components transfer: with the framework fixed, swapping in the trained model raises Match-SOTA from 50% to 70%, and with the model fixed, swapping in OpenMLE-Evo raises it from 20% to 50%.

The authors release the model weights and the full OpenMLE stack to enable reproducible research on executable AI4AI towards recursive self-improvement. The paper demonstrates the effectiveness of Frontis-MA1 and OpenMLE in achieving recursive self-improvement in machine learning engineering, and provides a foundation for further research in this area.

📅 Published on Jul 30

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2607.28568
• PDF: https://arxiv.org/pdf/2607.28568
• Project Page: https://frontisai.github.io/OpenRSI/

🤖 Models citing this paper:
• https://huggingface.co/FrontisAI/Frontis-MA1-35B-GGUF
• https://huggingface.co/FrontisAI/Frontis-MA1-30B
• https://huggingface.co/FrontisAI/Frontis-MA1-30B-GGUF

📊 Datasets citing this paper:
• https://huggingface.co/datasets/FrontisAI/OpenMLE-Tasks
• https://huggingface.co/datasets/FrontisAI/OpenMLE-SFT-Traces

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📢 By: https://t.iss.one/PaperNexus

#MachineLearningEngineering #ArtificialIntelligenceForAI #RecursiveSelfImprovement #MetaLearningAlgorithms #AIModelTraining

🔥 VideoCoCo: Code-as-CoT for Physically-Consistent Video Generation via an Agentic Dual-Engine System

💡 The paper introduces VideoCoCo, a novel framework for physically consistent video generation. The problem addressed is that existing text-to-video models struggle to generate videos with physically consistent dynamics, as they must infer the temporal evolution of a scene implicitly from a highly compressed text prompt. Current chain-of-thought approaches introduce intermediate plans or visual states, but these representations are typically non-executable or temporally sparse, limiting their ability to instantiate and control the complete spatiotemporal process.

To address this limitation, VideoCoCo uses an agentic dual-engine system, where executable Blender code serves as a process-level chain of thought. Given a text prompt, a coding agent synthesizes a Blender program that explicitly specifies the scene and its temporal evolution. The executable simulation engine runs the program to produce a deterministic spatiotemporal draft, which is then transformed into a photorealistic video by a generative video engine through draft-conditioned editing.

The authors also construct VideoCoCo-3K, a curated dataset of draft-instruction-target triplets to adapt the video editor to simulated drafts. The results demonstrate that VideoCoCo improves the OmniWeaving baseline from 0.475 to 0.558 on PhyGenBench and from 52.18 to 77.88 on VBench-2.0, achieving the best average score on both benchmarks. The findings show that executable code provides an effective, controllable, and inspectable intermediate representation for physically consistent video generation.

📅 Published on Jul 29

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2607.27380
• PDF: https://arxiv.org/pdf/2607.27380

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📢 By: https://t.iss.one/PaperNexus

#VideoGeneration #PhysicallyConsistentModeling #CodeAsCoT #AgenticDualEngine #TextToVideoSynthesis

🔥 INTACT: Isomorphic Intent-to-Action Learning for Search-Free World Models

💡 The paper introduces INTACT, a novel approach to learning world models that can predict how actions change a scene and recover actions for a desired change without expensive test-time search. The existing forward latent world models predict how actions change a scene, but recovering actions for a desired change is only possible through expensive test-time search. INTACT turns action-labeled, reward-free trajectories into a deployable intent-to-action interface. Each transition supplies physical intent, while a future goal supplies deployment intent. The architecture is isomorphic between local and goal motion-intent backbone-input graphs through an identical four-slot grammar and shared parameters.

The method provides intact transfer from RGB evidence to action-effective latent intent coordinates and from intent families to their corresponding action-law families. Asymmetric endpoint gradients ground physical successors and fix future goals as anchors, joining representation learning and control without pointwise latent matching or globally linear dynamics. The resulting coordinates support a robust distributional action law, where its conditional means serve directly as a search-free policy, while sampling remains available for diversity or optional verification.

The results show that one-epoch, zero-search models reach high success rates on four official tasks, achieving 85.78, 100.00, 97.67, and 97.89 percent success. Optional local cross-entropy method centered on the Direct plan reaches 96.86 percent macro success using 384 instead of 9000 candidate sequences, reducing sampling by 23.44 times while improving pure cross-entropy method by 16.00 points. One shared four-task encoder reaches 89.39 percent E5 Direct macro and improves every task over jointly trained models, while predicted expert action-family kNN tracks Direct success at 0.954. Direct inference takes 2.9-5.5 milliseconds. Overall, INTACT provides a robust and efficient approach to learning world models that can predict and recover actions for desired changes without expensive test-time search.

📅 Published on Jul 28

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2607.26056
• PDF: https://arxiv.org/pdf/2607.26056
• Project Page: https://zju3dv.github.io/INTACT-JEPA/

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📢 By: https://t.iss.one/PaperNexus

#IntentToActionLearning #WorldModels #IsomorphicLearning #SearchFreePlanning #LatentWorldModels

🔥 PhiZero: A World Model Built Around Physical Language

💡 The paper introduces PhiZero, a physical world model that uses a compact discrete representation of world-state transitions, referred to as physical language. Existing physical world models typically predict future videos directly in pixel space, which leaves the underlying world dynamics implicit within high-dimensional visual predictors. In contrast, PhiZero is motivated by humans' ability to abstract predictive structure from visual experience and organize it in natural language for explicit reasoning.

The method used in PhiZero involves learning physical language from in-the-wild videos through self-supervision and using it to explicitly reason about how the physical world evolves. PhiZero adopts a reason-then-render paradigm, where it first infers future world evolution as a physical-language sequence and then renders the inferred transitions into videos.

The results of extensive experiments across generation and understanding benchmarks validate the ability of PhiZero to model physically coherent world evolution. The paper also shows the potential of PhiZero for realistic and interactive world modeling, fine-grained action-conditioned simulation, and zero-shot motion transfer. Overall, PhiZero provides a new approach to physical world modeling that is based on a compact and discrete representation of world-state transitions, and has the potential to enable more efficient and effective modeling of complex physical systems.

📅 Published on Jul 30

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2607.28624
• PDF: https://arxiv.org/pdf/2607.28624
• Project Page: https://phi-zero.github.io/

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📢 By: https://t.iss.one/PaperNexus

#PhysicalLanguageModeling #WorldModelArchitecture #DiscreteRepresentationLearning #SelfSupervisedLearning #PhysicalWorldReasoning

🔥 On the Generalization of SFT: A Reinforcement Learning Perspective with Reward Rectification

💡 The paper addresses the limited generalization of Supervised Fine-Tuning SFT for Large Language Models LLMs compared to reinforcement learning. The authors analyze the standard SFT gradients and find that they implicitly encode a problematic reward structure that restricts the model's generalization capabilities. To overcome this, they propose Dynamic Fine-Tuning DFT, a simple yet effective method that stabilizes gradient updates for each token by dynamically rescaling the objective function with the probability of the token. This approach significantly outperforms standard SFT across multiple challenging benchmarks and base models, demonstrating greatly improved generalization. Additionally, DFT shows competitive results in offline reinforcement learning settings, providing a simpler alternative to existing methods. The authors provide a theoretical motivation for their approach and demonstrate its effectiveness through experiments, substantially advancing the performance of SFT. The proposed method is a single-line code change, making it easy to implement, and the code will be made available for further use. Overall, the paper contributes to the improvement of SFT for LLMs, bridging the gap between theoretical insights and practical solutions.

📅 Published on Aug 7, 2025

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2508.05629
• PDF: https://arxiv.org/pdf/2508.05629

🤖 Models citing this paper:
• https://huggingface.co/Naphula/Cthulhu-70B-v1
• https://huggingface.co/Liang0223/Qwen-2.5-Math-1.5B-DPO

📊 Datasets citing this paper:
• https://huggingface.co/datasets/egotools-dev/a100_20260502

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📢 By: https://t.iss.one/PaperNexus

#ReinforcementLearningForLLMs #SupervisedFineTuningLimitations #RewardRectificationTechniques #DynamicFineTuningMethods #LargeLanguageModelGeneralization

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🔥 Explorative Modeling: Unlocking a Third Pretraining Axis and End-to-End Generation

💡 The paper introduces Explorative Modeling, a new paradigm that unlocks a third pretraining axis for existing generative models, beyond parameters and data. The key problem addressed is that generative models are not trained end-to-end, due to the fact that they handle distributions with many modes, and existing scalable approaches factor the generation procedure, preventing end-to-end generation.

The proposed method, Explorative Modeling, factors the training loop instead, exploring K candidate matches between model generations and data, and training on the best predictions, which commit to modes rather than blurring them. This approach is found to be useful in two settings.

First, increasing exploration adds a third pretraining axis for existing generative models, where scaling exploration monotonically improves performance across both continuous and discrete domains, such as images, video, and language. Notably, gains from exploration increase with scale, climbing from 7 percent to 36 percent as data scales and from 13 percent to 23 percent as models grow, with efficiency gains more than doubling at three times the compute.

Second, Explorative Models enable end-to-end reconstructive generative modeling, matching diffusion on control tasks with 16-256 times fewer inference steps. The results establish Explorative Models as both a new pretraining axis for existing generative models and a standalone end-to-end generative modeling paradigm.

Overall, the paper's contributions include improving FLOP efficiency by 4.1 times, sample efficiency by 6.2 times, and parameter efficiency by 47 percent, and lifting the strongest image-generation recipes to near state-of-the-art 1.43 FID on ImageNet without guidance, enabling scaling and generalization.

📅 Published on Jul 29

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2607.27372
• PDF: https://arxiv.org/pdf/2607.27372
• Project Page: https://explorative-modeling.github.io/

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📢 By: https://t.iss.one/PaperNexus

#ExplorativeModeling #GenerativeModels #EndToEndGeneration #PretrainingTechniques #ModeDiscovery

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🔥 Amphion: An Open-Source Audio, Music and Speech Generation Toolkit

💡 The paper introduces Amphion, an open source toolkit for audio, music, and speech generation. The goal of Amphion is to support reproducible research and help junior researchers and engineers get started in the field. The toolkit provides model visualizations, which are beneficial for understanding classic models and architectures. Amphion also includes vocoders, which are essential for producing high quality audio signals, and evaluation metrics, which ensure consistent metrics in generation tasks. The toolkit is designed to support individual generation tasks and offers a platform for studying the conversion of any inputs into general audio. The key contribution of Amphion is its ability to provide a comprehensive platform for audio, music, and speech generation, making it easier for researchers to conduct reproducible research and for junior researchers to enter the field. The paper provides a high level overview of Amphion, highlighting its features and capabilities, and demonstrating its potential to support research and development in audio, music, and speech generation.

📅 Published on Dec 15, 2023

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2312.09911
• PDF: https://arxiv.org/pdf/2312.09911

📊 Datasets citing this paper:
• https://huggingface.co/datasets/Pendrokar/open_tts_tracker

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📢 By: https://t.iss.one/PaperNexus

#AudioGenerationTools #MusicGenerationTechniques #SpeechSynthesisSystems #OpenSourceAudioToolkits #VocoderTechnology

🔥 Transcription Policy as a Latent Variable: Activating Controllable Verbatim ASR with Word-Level Timing

💡 The paper addresses the issue of transcription policy as a latent variable in automatic speech recognition models, which can cause decoding instability, evaluation confounding, and unreliable word-level timing. Modern speech recognition models are trained on heterogeneously annotated data, treating transcription style as an uncontrolled latent variable. The authors show that models already encode both verbatim and intended transcription styles, but the challenge lies in controlled activation.

To address this issue, the authors propose using a coverage-aware decoder task with tokens trained on parallel verbatim and intended transcript pairs. This approach raises German disfluency F1 from 10 to 79 percent zero-shot, despite English-only training. Full English-only fine-tuning surpasses all baselines in verbatim accuracy, disfluency detection, and intended-mode quality across both languages.

The authors further introduce supervised cross-attention fine-tuning, which improves word-level timestamps on disfluent speech beyond forced-alignment baselines. Finally, they propose Verbatimize, a new task enabling scalable creation and enrichment of speech corpora with high-quality canonical verbatim transcripts. The results demonstrate significant improvements in speech recognition and transcription quality, with potential applications in speech processing and related fields.

📅 Published on Jul 21

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2607.18934
• PDF: https://arxiv.org/pdf/2607.18934
• Project Page: https://nyra-labs.com

🤖 Models citing this paper:
• https://huggingface.co/nyralabs/CrisperWhisper2.0_large
• https://huggingface.co/nyralabs/CrisperWhisper2.0_small
• https://huggingface.co/nyralabs/CrisperWhisper2.0_turbo

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/nyralabs/crisperwhisper2-controllable-asr

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📢 By: https://t.iss.one/PaperNexus

#AutomaticSpeechRecognition #TranscriptionPolicy #LatentVariablesInASR #WordLevelTiming #ControllableASR