Molecular property prediction is fundamental to drug discovery and materials science, yet existing models suffer from limited interpretability and poor cross-task generalization. Without explainable chemical rationales, accurate predictions alone are insufficient for guiding costly downstream experiments. We propose MppReasoner, a multimodal large language model that incorporates verifiable chemical reasoning for molecular property prediction. Built on Qwen2.5-VL-7B-Instruct, MppReasoner integrates 2D molecular images with SMILES strings through a two-stage training strategy: supervised fine-tuning (SFT) on 16,000 expert-guided reasoning trajectories, followed by Reinforcement Learning from Principle-Guided Rewards (RLPGR). RLPGR applies rule-based rewards verified by RDKit to evaluate chemical principle usage, structural analysis, and logical consistency, providing objective feedback without human preference labels. Experiments across 8 benchmark datasets show that MppReasoner outperforms the strongest baseline by 2.95% and 5.49% on in-distribution and out-of-distribution tasks, respectively. Beyond predictive accuracy, MppReasoner generates chemically grounded reasoning traces, with expert evaluation confirming high reasoning quality across three assessed dimensions. Code is available at https://github.com/Jesse-zjx/MPPReasoner.

Multi-objective alignment for text-to-image generation is commonly implemented via static linear scalarization, but fixed weights often fail under heterogeneous rewards, leading to optimization imbalance where models overfit high-variance, high-responsiveness objectives (e.g., OCR) while under-optimizing perceptual goals. We identify two mechanistic causes: variance hijacking, where reward dispersion induces implicit reweighting that dominates the normalized training signal, and gradient conflicts, where competing objectives produce opposing update directions and trigger seesaw-like oscillations. We propose APEX (Adaptive Priority-based Efficient X-objective Alignment), which stabilizes heterogeneous rewards with Dual-Stage Adaptive Normalization and dynamically schedules objectives via 𝒫³ Adaptive Priorities that combine learning potential, conflict penalty, and progress need. On Stable Diffusion 3.5, APEX achieves improved Pareto trade-offs across four heterogeneous objectives, with balanced gains of +1.31 PickScore, +0.35 DeQA, and +0.53 Aesthetics while maintaining competitive OCR accuracy, mitigating the instability of multi-objective alignment.

The composition of pre-training datasets for large language models (LLMs) remains largely undisclosed, hindering transparency and efforts to optimize data quality—a critical driver of model performance. Current data selection methods, such as natural language quality assessments, diversity-based filters, and classifier-based approaches, are limited by single-dimensional evaluation or redundancy-focused strategies. To address these gaps, we propose four dimensions to evaluate data quality: professionalism, readability, reasoning, and cleanliness. We further introduce Meta-rater, a multi-dimensional data selection method that integrates these dimensions with existing quality metrics through learned optimal weightings. Meta-rater employs proxy models to train a regression model that predicts validation loss, enabling the identification of optimal combinations of quality scores. Experiments demonstrate that Meta-rater doubles convergence speed for 1.3B parameter models and improves downstream task performance by 3.23%, with advantages that scale to models as large as 7.2B parameters. Our work establishes that holistic, multi-dimensional quality integration significantly outperforms conventional single-dimension approaches, offering a scalable paradigm for enhancing pre-training efficiency and model capability. To advance future research, we release scripts, data, and models at https://github.com/opendatalab/Meta-rater.