TTRSD: Test-Time Reinforcement Learning with Self-Distillation for Vision-Language Models
cs.CV, cs.AI
Submitted: 2026-09-27
Updated: 2026-09-27
Terminology
Sources
- GPT-4 Technical Report
- LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training
- Qwen3-VL Technical Report
- InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model
- MedHorizon: Towards Long-context Medical Video Understanding in the Wild
- Phi-3 Safety Post-Training: Aligning Language Models with a "Break-Fix" Cycle
- Rethinking On-Policy Self-Distillation for Thinking Models
- LLaVA-OneVision: Easy Visual Task Transfer
- Rethinking Token-Level Policy Optimization for Multimodal Chain-of-Thought
- Beyond Frame Selection: Generative Latent Evidence Aggregation for Long-Video Understanding
- DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
- Gemini: A Family of Highly Capable Multimodal Models
- TTPO: Test-Time Policy Optimization
- Tent: Fully Test-time Adaptation by Entropy Minimization
- See More, Think Deeper: Query-Expanded Visual Evidence and Answer-Clue Guided Reflection for Long Video Understanding
- See Further, Think Deeper: Advancing VLM's Reasoning Ability with Low-level Visual Cues and Reflection
- LogicVista: Multimodal LLM Logical Reasoning Benchmark in Visual Contexts
- Self-Distilled RLVR
- MiniCPM-V: A GPT-4V Level MLLM on Your Phone
- Not All Tokens See Equally: Perception-Grounded Policy Optimization for Large Vision-Language Models
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