The field of medical imaging and personalized marketing is witnessing significant developments with the emergence of small language models (SLMs) and large language models (LLMs). Researchers are exploring the use of SLMs in medical imaging classification tasks, such as mammogram visual question answering and chest X-ray classification, with promising results. The use of prompt engineering and contrastive learning-based fine-tuning is shown to enhance the performance of SLMs in these tasks. Additionally, SLMs are being applied to personalized marketing, with models such as SLM4Offer and Trained Miniatures demonstrating improved offer acceptance rates and cost-effectiveness.

In the field of LLMs, recent developments have centered around improving alignment and optimization techniques. Approaches such as survey-to-behavior alignment and reward-guided decoding are showing promise in enhancing the ability of LLMs to incorporate human values and preferences. Multimodal LLMs are also being explored, with techniques like input-dependent steering and multi-objective alignment via value-guided inference-time search being investigated.

The application of LLMs to specific domains, such as tourism and e-commerce, has also shown significant potential. Novel frameworks for evaluating and optimizing the performance of LLMs, such as chain-of-thought reasoning and expert-guided optimization, have been introduced. Furthermore, advancements in synthetic data generation and management have enabled the creation of high-quality datasets for training and fine-tuning LLMs.

Recent developments in reinforcement learning (RL) have highlighted the importance of balancing exploration and exploitation. Innovative approaches, such as entropy-based mechanisms and adaptive guidance, have demonstrated significant improvements in performance on various benchmarks. The introduction of evolutionary testing and automated benchmark generation has also enabled the creation of more challenging and diverse evaluation instances, pushing the boundaries of LLMs' reasoning capabilities.

Notable papers in these areas include Is ChatGPT-5 Ready for Mammogram VQA?, Applications of Small Language Models in Medical Imaging Classification with a Focus on Prompt Strategies, SLM4Offer: Personalized Marketing Offer Generation Using Contrastive Learning Based Fine-Tuning, Survey-to-Behavior: Downstream Alignment of Human Values in LLMs via Survey Questions, and Controlling Multimodal LLMs via Reward-guided Decoding. Other notable papers include LETToT, TaoSR1, OS-R1, CURE, ETTRL, EvolMathEval, G$^2$RPO-A, Depth-Breadth Synergy in RLVR, Beyond Pass@1, Hard Examples Are All You Need, and Your Reward Function for RL is Your Best PRM for Search. Overall, these advances have the potential to significantly improve the performance and safety of LLMs and SLMs in a wide range of applications.