While RLVR is effective for deterministically verifiable tasks, many vision-language tasks are only *partially verifiable*, demanding multi-criterion supervision. To bridge this gap, we propose *Reinforcement Learning with Robust Rubric Rewards (RLR³), extending RL with criterion-level rewards. To ensure faithful scoring, RLR³ routes each criterion to one of two execution paths: an LLM-as-an-extractor paired with a verifier, or an LLM-as-a-Judge for fuzzy criteria. We also introduce a minimal exposure strategy that masks verifier targets from extractors and source images from the GenRM. Furthermore, RLR³ employs decoupled normalization to mitigate score saturation and hierarchical aggregation to prioritize essential criteria. Evaluated on Qwen3-VL-30B-A3B across 15 benchmarks, RLR³ consistently outperforms RLVR, yielding a 4.7-point improvement over the base model and exceeding the official instruct-to-thinking model gap. Controlled audits show that deterministic verifier and minimal exposure strategy reduce exploitable false positives.

The effectiveness of Direct Preference Optimization (DPO) depends on data pairs that capture task-relevant quality differences. Existing data pipelines often rely on *off-policy* perturbations or *coarse* outcome-based signals, which are not well suited to fine-grained visual reasoning. We propose *rDPO*, a rubric-based data pipeline for DPO-based visual preference optimization. For each image-instruction pair, we create a checklist-style rubric of essential and additional criteria to score responses from any possible policies. The instruction-rubric pool is built *offline* and reused during the construction of *on-policy* preference data. On public reward modeling benchmarks, rubric-based prompting massively improves a 30B-A3B judge and brings it close to GPT-5.4. On public downstream benchmarks, rubric-based filtering raises the macro average to 82.69, whereas outcome-based filtering drops it to 75.82 from 81.14. In scalability evaluation on a comprehensive benchmark, rDPO achieves 61.01, markedly outperforming the style-constrained baseline (52.36) and surpassing the 59.48 base model. Together, these results show that visual preference optimization benefits from combining on-policy data construction with instance-specific criterion-level feedback.