JoyInside-VLM-Agent: Product-Conditioned Multimodal Policy Learning for Embodied Agents
cs.RO, cs.AI, cs.LG
Submitted: 2026-10-09
Updated: 2026-10-09
Comments: 23 pages, 6 figures, 5 tables; includes references and appendices
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- Open X-Embodiment: Robotic Learning Datasets and RT-X Models
- Self-Distilled RLVR
- DOPD: Dual On-policy Distillation
- $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains
- $\tau^2$-Bench: Evaluating Conversational Agents in a Dual-Control Environment
- Agent-R: Training Language Model Agents to Reflect via Iterative Self-Training
- Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning
- Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models
- H-OPD: Confidence Aware Heterogeneous Multi-Teacher Multimodal On-policy Distillation
- Scaling the Horizon, Not the Parameters: Reaching Trillion-Parameter Performance with a 35B Agent
- RL Starts before RL: On Policy Distillation for Better Reinforcement Learning
- Let's Verify Step by Step
- Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models
- Self-Instruct: Aligning Language Models with Self-Generated Instructions
- ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilities
- APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets
- From Absolute to Relative: Rethinking Reward Shaping in Group-Based Reinforcement Learning
- LLaVA-Video: Video Instruction Tuning With Synthetic Data
- Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
- VitaBench: Benchmarking LLM Agents with Versatile Interactive Tasks in Real-world Applications
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