OphVLM-R1

Overview

OphVLM-R1 is a lightweight 2B-parameter vision-language model for ophthalmic multimodal reasoning. Starting from InternVL3.5-2B, it first acquires ophthalmic knowledge through LoRA supervised fine-tuning (SFT), then develops progressively harder clinical reasoning skills through four-stage curriculum reinforcement learning. Group Sequence-level Policy Optimization (GSPO) and hard-sample dynamic backtracking are used to improve optimization over long reasoning trajectories and difficult long-tail cases.

This README focuses on the model training pipeline, architecture and algorithms, and experimental results. For dataset construction and distribution details, see the OphReason-Vision dataset repositories below.

Project Resources

• Project page: https://qizishi.github.io/OphVLM-R1/
• OphVLM-R1 model: Hugging Face | ModelScope
• OphReason-Vision dataset: Hugging Face | ModelScope
• OphAgent system: GitHub

Model and Training Framework

OphVLM-R1 uses InternVL3.5-2B as its backbone. Its 2B parameter scale targets deployment in resource-constrained settings while retaining multimodal clinical reasoning capacity. Training consists of two stages.

Stage 1: LoRA Supervised Fine-Tuning

The cold-start stage injects ophthalmic domain knowledge with Low-Rank Adaptation:

$$ W_0 + \Delta W = W_0 + BA, \qquad h = W_0x + \frac{\alpha}{r}BAx $$

where \(B \in \mathbb{R}^{d \times r}\), \(A \in \mathbb{R}^{r \times k}\), and \(r \ll \min(d,k)\). The SFT objective is

$$ \mathcal{L}{\mathrm{SFT}}=-\mathbb{E}\left[\sum{t=1}^{\lvert y\rvert}\log\pi_\phi(y_t\mid x,y_{\lt t})\right]. $$

Stage 2: Curriculum Reinforcement Learning

GSPO computes the policy ratio at sequence level rather than independently clipping token-level ratios:

$$ s_i(\phi)=\left(\frac{\pi_\phi(y_i\mid x)}{\pi_{\phi_{\mathrm{old}}}(y_i\mid x)}\right)^{\frac{1}{\lvert y_i\rvert}}=\exp\left(\frac{1}{\lvert y_i\rvert}\sum_{t=1}^{\lvert y_i\rvert}\log\frac{\pi_\phi(y_{i,t}\mid x,y_{i,\lt t})}{\pi_{\phi_{\mathrm{old}}}(y_{i,t}\mid x,y_{i,\lt t})}\right). $$

The PPO-style objective uses group-normalized advantages:

$$ \mathcal{J}{\mathrm{GSPO}}(\phi)=\mathbb{E}\left[\frac{1}{G}\sum{i=1}^{G}\min\left(s_i\hat A_i,\operatorname{clip}(s_i,1-\varepsilon,1+\varepsilon)\hat A_i\right)\right]. $$

Each stage uses a mixed reward

$$ r(x,y)=\lambda_1r_{\mathrm{rule}}(x,y)+\lambda_2r_{\mathrm{judge}}(x,y), $$

where the judge reward is produced by Intern-S1-mini. The curriculum follows increasing clinical complexity:

1. Lesion Localization — single-image visual perception.
2. Multi-image Selection — cross-image comparison.
3. Report Generation — structured, long-form synthesis.
4. Knowledge Q&A — integration of visual findings and clinical knowledge.

Hard-Sample Dynamic Backtracking

Prompts that repeatedly receive low rewards during the most recent \(k=5\) rounds are resampled on-policy:

$$ P_{\mathrm{sample}}(x,y)\propto f(x,y)^\beta, $$

where \(f(x,y)\) is the consecutive failure count and \(\beta=2.0\). Resampled prompts always receive fresh rollouts, and the resampling share is capped at 30% of each batch.

Training Stack

• Hardware: 8 NVIDIA GeForce RTX 4090 GPUs (24 GB each).
• SFT: 4 GPUs.
• Reinforcement learning: 6 GPUs for training and 2 GPUs for vLLM rollout generation.
• Software: ms-swift, DeepSpeed ZeRO-3, AdamW, vLLM, and EvalScope.

Experiments

Evaluation Benchmarks

Main Results

Accuracy is reported in percent. The cross-benchmark average is reference-only because the benchmarks differ in task format, difficulty, and random baseline; per-benchmark comparisons are primary.

OphVLM-R1 reaches 88.24% on OmniMedVQA-Eye and 42.58% on Fundus-MMBench. Its 56.41% reference average is 4.46 percentage points above InternVL3.5-4B and 2.30 points above OphthaReason-Qwen-3B. Comparisons with off-the-shelf 7B/8B models should be interpreted cautiously because data exposure and parameter scale are not controlled.

Ablation Results

The ablations show that ordered curriculum RL, sequence-level optimization, and hard-sample backtracking make independent contributions. All reported results are single runs without confidence intervals or significance tests. In addition, the theoretical variance-reduction argument for GSPO is exact only under i.i.d. per-token log-ratio assumptions and is approximate for correlated natural-language tokens.

Citation

@inproceedings{qi2026ophvlm,
  title={OphVLM-R1: Efficient Ophthalmic Reasoning via Curriculum Reinforcement Learning},
  author={Qi, Zishi and Hu, Xiaoya and Pan, Huilin and Gao, Ang and Hou, Jiaxin and Li, Jiankun and Qian, Yongao},
  booktitle={Proceedings of the World Artificial Intelligence Conference Academic (WAICA)},
  year={2026}
}

Acknowledgements

We thank OpenGVLab for the InternVL foundation model and the ophthalmologists who contributed to data review and quality assurance.