We introduce ThinkLite-VL, a family of visual reasoning models that achieve state-of-the-art (SoTA) performance using an order of magnitude fewer training samples, relying purely on reinforcement fine-tuning (RFT) self-improvement without any knowledge distillation. Our central insight is that sample difficulty critically influences RFT effectiveness: appropriately challenging examples can drive substantial reasoning improvements, even in low-data regimes. However, quantifying sample difficulty in a reliable and scalable manner remains non-trivial. To address this, we repurpose Monte Carlo Tree Search (MCTS) to measure sample difficulty via the number of reasoning iterations a vision-language model (VLM) requires to solve each instance. This MCTS-based selection procedure identifies samples that induce deeper reasoning while remaining solvable, allowing us to filter a high-quality subset from 70k open-source examples spanning math, natural image understanding, and chart comprehension. Using this approach, we select just 11k challenging samples for RFT on Qwen2.5-VL-7B-Instruct and 7.5k samples for Qwen2.5-VL-72B-Instruct. The resulting models, ThinkLite-VL-7B and ThinkLiteVL-72B, significantly outperform their respective base models across eight visual reasoning benchmarks.

Fairness and robustness are critical elements of Trustworthy AI that need to be addressed together. Fairness is about learning an unbiased model while robustness is about learning from corrupted data, and it is known that addressing only one of them may have an adverse affect on the other. In this work, we propose a sample selection-based algorithm for fair and robust training. To this end, we formulate a combinatorial optimization problem for the unbiased selection of samples in the presence of data corruption. Observing that solving this optimization problem is strongly NP-hard, we propose a greedy algorithm that is efficient and effective in practice. Experiments show that our algorithm obtains fairness and robustness that are better than or comparable to the state-of-the-art technique, both on synthetic and benchmark real datasets. Moreover, unlike other fair and robust training baselines, our algorithm can be used by only modifying the sampling step in batch selection without changing the training algorithm or leveraging additional clean data.

The pretraining-finetuning paradigm has gained widespread adoption in vision tasks and other fields. However, the finetuning phase still requires high-quality annotated samples. To overcome this challenge, the concept of active finetuning has emerged, aiming to select the most appropriate samples for model finetuning within a limited budget.

The pretraining-finetuning paradigm has gained widespread adoption in vision tasks and other fields. However, the finetuning phase still requires high-quality annotated samples.

Inlabel-noiselearning,thenoise transitionmatrix,bridgingtheclassposteriorfor noisy and clean data, has been widely exploited to learnstatistically consistent classifiers.

Theorem1.Let Pi represent the distribution of all seen training samples (including all previous Agoodtrade-offbetween themodel'scomplexityandgeneralization performance, observedfrom Eq. (12), is allowing each component to learn the underlying data distribution of a unique target set. By satisfying the ideal selection process (Eq.(22) of the paper) and also consideringthateachcomponent Gtfinishedthetrainingon Mkt atTkt,weassumethatthedynamic 4 expansion modelG can be seen as a single modelh trained on all previously learnt memories Maximal Interfered Retrieval (MIR), [1] is one of 5 themostpopular memory-based approaches, whichusesamemory bufferwithasample selection criterion. Since Pi would involve several underlying data distributions as the number of training steps (i) increases, the diversity in the memory plays an important role to ensure a tight GB in Eq.(15). G be single model which consists of a classifierh HandaVAEmodelv. M be a memory buffer updated at the training stepTi. Figure 1: The learning process of the proposed ODDL-S, which consists of three phases.

A continual learning agent learns online with a non-stationary and never-ending stream of data. The key to such learning process is to overcome the catastrophic forgetting of previously seen data, which is a well known problem of neural networks. To prevent forgetting, a replay buffer is usually employed to store the previous data for the purpose of rehearsal. Previous work often depend on task boundary and i.i.d.