Papers Instruction Following
“Instruction Following” 태그가 달린 논문 1,603편 · 필터 해제
SenseNova-U1.5: Towards Native Unified Visual Intelligence
We launch SenseNova-U1.5, an 8B-MoT native unified multimodal model that understands, reasons about, and generates visual content within an encoder-free and VAE-free architecture. We strengthen its visual interface throu…
Reinforcement LearningInstruction FollowingImage EditingVidu S2: Real-Time Interactive, Editable, and Spatial Video Generation
We present Vidu S2, which comprises Vidu S2-Avatar, a real-time interactive digital-character model, and Vidu S2-Editing, a real-time video editing model. Moreover, we explore the feasibility of real-time spatial video g…
Instruction FollowingVideo GenerationBuilding Multilingual Bridges: Data Mixing as the Pillar of Generalization for In-Language Reasoning
Reasoning language models have made substantial advances on a variety of complex tasks, yet their capabilities remain overwhelmingly English-centric: models primarily reason in English regardless of the language they are…
Instruction FollowingNeoHorse-1: Towards Recursive Self-Improvement via Agentic Post-Training with Routing Harness
Recursive self-improvement (RSI) requires a concrete mechanism through which an AI system observes its capabilities and converts that evidence into the next round of learning. We present NeoHorse-1, a family of agent-nat…
Instruction FollowingMobileVLA-R1 2.0: RL-Enhanced Reasoning for Mobile Robot Control
Grounding natural-language instructions into reliable and executable actions remains a fundamental challenge for vision-language-action (VLA) systems on mobile robots, due to the persistent gap between high-level semanti…
Reinforcement LearningInstruction FollowingMultimodal ReasoningDecision MakingEuroAlpaca: Task-Preserving Localisation of Instruction Data for European Languages
Machine translation (MT) offers a scalable way to extend English instruction-tuning data to multiple languages, but it can distort task-critical constraints and required outputs, creating corrupted training examples and …
Instruction FollowingMachine TranslationLearning 3D Editing without Paired Supervision via Generative Prior Distillation
Instruction-guided 3D editing is essential for interactive content creation, yet it faces a significant bottleneck: the severe scarcity of high-quality paired training data. Existing approaches attempt to bypass this by …
Instruction FollowingImage EditingOccamy-1.0: Open Pareto-frontier 35B Intelligence for Co-work
Co-work agents execute complex workflows that combine information gathering, tool use, coding, and file manipulation across many model invocations. Because cost and latency accumulate over the full episode, their practic…
Instruction FollowingVerify Before You Distill: Prompt-Level Teacher Gating for On-Policy Distillation
On-policy distillation (OPD) accelerates post-training by providing dense token-level supervision from a frozen teacher on the student's own rollouts. Vanilla OPD applies this supervision uniformly across prompts, withou…
Instruction FollowingFrom Production Traffic to Post-Training: Building a Self-Hosted LLM That Covers the Corporate Request Mix
Data-residency constraints force enterprises to self-host LLMs, but continuous adoption of newer models without decommissioning their predecessors expands the serving fleet, fragmenting a finite GPU pool. We consolidate …
Instruction FollowingReading the News: Adapting Large Language Models to Swedish Journalism Through Continued Pre-Training
Large language models are increasingly capable in general, but their utility can remain modest in niche or understudied areas. One approach to address this limitation is to specialise existing models through additional t…
parameter-efficient fine-tuningInstruction FollowingVIBE: Video Instruction-aligned Background music gEneration
Current video-to-music (V2M) models lack semantic control and fail to penalize instruction violations, largely due to their reliance on reconstruction objectives and the representational bottleneck of static cross-modal …
Instruction FollowingMusic GenerationLightNav-0: Eliciting VLM Spatial Intelligence for Generalist Embodied Navigation
Embodied navigation requires agents to translate heterogeneous goals and visual observations into actions across tasks, environments, and robot embodiments. Modern vision-language models (VLMs) already encode spatial pri…
Zero-shot GeneralizationReinforcement LearningInstruction FollowingSpatial ReasoningDARTS: Decoder-Aware Representation Tuning via Surgery for Model Merging
Model merging combines multiple task-specific fine-tuned LLMs into a single multi-task model without additional training. However, merged models are known to suffer from representation bias: systematic drift between the …
Mathematical ReasoningInstruction FollowingCode GenerationPreserving General Capabilities during Domain Specialization with Uncertainty-Calibrated MOPD
Specializing large language models to vertical domains improves domain-specific behavior but often degrades general capabilities such as reasoning, coding, instruction following, and creative writing. We study this domai…
Instruction FollowingVISA: Agentic Self-Evolving Data Synthesis for Multimodal Instruction Following
Multimodal instruction-following models require training data that is accurate, diverse, verifiable, and challenging. Existing synthesis pipelines typically follow a one-pass generate-and-filter paradigm, discarding feed…
Reinforcement LearningInstruction FollowingSciMIF: Understanding Multimodal Instruction Following in Scientific Domains
Understanding instruction-following capabilities in scientific domains is essential for effectively leveraging Multimodal Large Language Models (MLLMs) to advance the development of scientific fields. In this work, we in…
Instruction FollowingMA-VLA: Multi-Arm Vision-Language-Action Model for Collaboration and Compositional Generalization
Multi-arm collaboration is becoming a core capability in embodied manipulation. Recent vision-language-action (VLA) models integrate perception, language, and control, but most represent language as a single global instr…
Instruction FollowingV-Rubrics: Visual Faithfulness via Rubric-Based Reinforcement Learning
Vision-language models can produce fluent answers that are insufficiently grounded in the visual evidence: a single unsupported object, chart value, or intermediate inference can undermine an otherwise plausible response…
Reinforcement LearningInstruction FollowingVideo-IFBench: Evaluating Instruction Following of Multimodal LLMs in Video Understanding Scenarios
Multimodal Large Language Models (MLLMs) have shown strong performance in video understanding. However, their ability to follow instructions in this domain remains under-explored. Real-world video understanding requires …
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