WaltonFuture/Qwen2.5VL-7b-RLCS
WaltonFuture/Qwen2.5VL-7b-RLCS is a 7 billion parameter multimodal model developed by WaltonFuture, built upon the Qwen2.5-VL architecture. This model is specifically fine-tuned using a two-stage process involving supervised fine-tuning (SFT) with a 'cold start' dataset and further optimization via Reinforcement Learning (RL) with GRPO. It is designed to advance multimodal reasoning capabilities, particularly in scenarios requiring robust initial learning and subsequent refinement.
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Overview
WaltonFuture/Qwen2.5VL-7b-RLCS is a 7 billion parameter multimodal model developed by WaltonFuture, designed to enhance multimodal reasoning through a novel two-stage training approach. Built on the Qwen2.5-VL backbone, this model leverages both supervised fine-tuning (SFT) and Reinforcement Learning (RL) to achieve improved performance. The training methodology, detailed in the paper "Advancing Multimodal Reasoning via Reinforcement Learning with Cold Start" (arXiv:2505.22334), addresses the challenge of cold start in RL for multimodal tasks.
Key Capabilities
- Enhanced Multimodal Reasoning: Optimized for complex reasoning tasks involving both visual and linguistic inputs.
- Two-Stage Training: Utilizes a "Cold Start" supervised fine-tuning phase followed by Reinforcement Learning (RL) using GRPO for refined performance.
- Qwen2.5-VL Foundation: Benefits from the robust architecture and pre-training of the Qwen2.5-VL model family.
- Dedicated Datasets: Trained with specific datasets, Multimodal-Cold-Start for SFT and Multimodal-RL-Data for RL, tailored for its training methodology.
Good for
- Researchers and developers exploring advanced multimodal reasoning techniques.
- Applications requiring robust performance from initial learning stages through iterative refinement.
- Experiments with Reinforcement Learning in multimodal contexts, particularly those addressing cold start challenges.
- Building upon the Qwen2.5-VL architecture for specialized multimodal tasks.