Papers Instruction Following
“Instruction Following” 태그가 달린 논문 1,613편 · 필터 해제
UniWAM: Unified World-Action Model
Vision-language-action models benefit from the understanding and reasoning capabilities of pretrained vision-language models, but action-only supervision provides limited grounding in world dynamics. Conversely, world-ac…
Visual Question AnsweringInstruction FollowingVideo GenerationQuantCode Model: Specializing Language Models for Executable Algorithmic Trading Code
Large language models are strong general-purpose code generators, but executable algorithmic trading remains a demanding specialization target: a model must translate a natural-language strategy specification into correc…
Instruction FollowingCode GenerationSystematically Exploring the Capabilities of GPT-6 Astra as Embodied Policies
GPT-6 Astra exhibits a remarkable ability to generate numerical robot actions, extending its role beyond high-level planning. To assess Astra's capabilities as general-purpose embodied policies, we conduct comprehensive …
Instruction FollowingROSS: Relearning from Self-Generated Rollouts through Selective Supervision
Large language model post-training generates self-generated rollouts through reinforcement learning and on-policy distillation, yet this experience is often treated as stale once the policy advances. Historical rollouts …
Reinforcement LearningInstruction FollowingCode GenerationDuoOPD: Learning from Joint Teacher-Student Outcomes for Multi-Task On-Policy Distillation
On-policy distillation (OPD) trains a student on its own responses with token-level feedback from a stronger teacher, yet the teacher can fail on questions the student already answers correctly, and how often each model …
Instruction FollowingCode GenerationTimeBraid: Unifying Time Series and Language for Understanding and Forecasting
We present TimeBraid, a series of unified time-series and language models that align pretrained language models and pretrained time-series foundation models through interleaved global residual attention layers. Each mode…
Instruction FollowingRoboFollow: Unveiling the Instruction Following Mirage in Embodied Agents
Modern embodied agents achieve impressive success rates, yet their actual instruction-following ability is far weaker than these numbers suggest. We trace this illusion to a structural property we term low scene entropy:…
Instruction FollowingGeometry of Values: Task Vector Composition for Ethical Preference Alignment in Language Models
Large Language Models (LLMs) are increasingly deployed in applications that must weigh clashing moral values, yet even strong models exhibit hidden biases and brittle instruction-following across languages. We introduce …
Instruction FollowingLynnReal-Omni: Native multi-modal Video Generation for Agentic Visual Workflows
Video diffusion models are stochastic and hard to control: precise content often requires repeated sampling without guaranteed success, and long-horizon scenes drift in appearance, interactions, and temporal coherence. A…
Instruction FollowingVideo RestorationVideo GenerationSteerDuplex: Steerable Duplex Speech Dialogue Models
Full-duplex spoken dialogue models support low-latency turn taking, interruption handling, and backchanneling, yet a key capability remains underexplored: steerability, the ability to reliably shift conversational behavi…
Reinforcement LearningInstruction FollowingSenseNova-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 …
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