davanstrien/qwen35-4b-iconclass-grpo-v5hyb
The davanstrien/qwen35-4b-iconclass-grpo-v5hyb is a 4.5 billion parameter Qwen3.5-4B-VL model, derived from davanstrien/qwen35-4b-iconclass-vlm. This model was developed by davanstrien as an experimental checkpoint for iconclass classification, specifically testing a GRPO reward-ablation configuration. It focuses on evaluating whether a richer reward bundle (gt_match + count + diversity) improves performance over plain hierarchical-F1 for this specialized visual classification task.
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Model Overview
The davanstrien/qwen35-4b-iconclass-grpo-v5hyb is a 4.5 billion parameter model based on the Qwen3.5-4B-VL architecture. It is an experimental checkpoint derived from davanstrien/qwen35-4b-iconclass-vlm, developed by davanstrien.
Experimental Focus
This model was created as part of an experiment to test the effectiveness of a richer reward bundle in Grouped Reward Policy Optimization (GRPO) for iconclass classification. The specific reward configuration used was gt_match + count + diversity, aiming to improve upon plain hierarchical-F1 (gt_match) performance.
Performance and Findings
- Completeness-corrected H-F1 (40-image test): 61.7%
- Verdict: The experiment concluded that this reward configuration showed no significant improvement over plain
gt_matchfor iconclass classification. All tested variants achieved similar scores (61–64%), indicating that the model's capabilities, rather than the reward tuning, were the limiting factor. The README suggests that "anchored fusion" (as seen inqwen35-4b-iconclass-sft-brillfull) was a more effective approach for this task.
Training Details
The model was trained using Unsloth and TRL, building upon the davanstrien/qwen35-4b-iconclass-vlm base.