Reinforcement Learned Teachers (RLT) train teacher models to generate clear explanations from question-answer pairs, enhancing student models' understanding. This innovative approach allows compact teacher models to outperform larger ones in reasoning tasks, significantly reducing training costs and times while maintaining effectiveness. The framework shifts the focus from problem-solving to teaching, promising advancements in AI reasoning models.

JudgeLRM introduces a novel approach to using Large Language Models (LLMs) as evaluators, particularly in complex reasoning tasks. By employing reinforcement learning with judge-wise rewards, JudgeLRM models significantly outperform traditional Supervised Fine-Tuning methods and current leading models, demonstrating superior performance in tasks that require deep reasoning.

InternVL3.5 introduces a new family of open-source multimodal models that enhance versatility, reasoning capabilities, and inference efficiency. A key innovation is the Cascade Reinforcement Learning framework, which improves reasoning tasks significantly while a Visual Resolution Router optimizes visual token resolution. The model achieves notable performance gains and supports advanced capabilities like GUI interaction and embodied agency, positioning it competitively against leading commercial models.

Reinforcement Learning on Pre-Training Data (RLPT) introduces a new paradigm for scaling large language models (LLMs) by allowing the policy to autonomously explore meaningful trajectories from pre-training data without relying on human annotations for rewards. By adopting a next-segment reasoning objective, RLPT improves LLM capabilities, as demonstrated by significant performance gains on various reasoning benchmarks and encouraging broader context exploration for enhanced generalization.