Background to the issue
Traditional VLMs often suffer from sudden performance degradation in cross-domain tasks, and R1-V enables the model to obtain excellent generalization capabilities with small amounts of data by designing verifiable reward functions.
Key technologies
- Dynamic reward calculation::
- Image-Text Alignment Score (CLIP Similarity)
- Logical conformance validation (through a network of small validators)
- Conceptual coverage assessment (based on attention mechanism analysis)
- Multi-stage reinforcement::
- Elementary level: reinforcement of basic object recognition
- Intermediate Level: Enhanced Understanding of Spatial Relationships
- Advanced level: reinforcement of complex reasoning skills
Method of implementation
- Prepare validation sets containing 5-10 cross-domain tasks
- Customize the rewards function in r1v/rewards.py:
- Adding domain adaptation scoring items
- Setting dynamic reward weighting factors
- Loading pre-trained models using the model.finetune() interface
- 3-5 iterations of reinforcement via RLHF pipeline
Effectiveness Verification
The following assessment program is recommended:
- Testing aesthetic scores on the unseen Aesthetics dataset
- Assessing Reasoning Skills Using VCR Benchmarks
- Testing Combinatorial Generalizability with Winoground
This answer comes from the articleR1-V: Low-Cost Reinforcement Learning for Visual Language Model Generalization CapabilitiesThe
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