Reinforcement learning for language models usually relies on verifiable rewards, where a system can objectively check if an answer is correct, such as in mathematics or computer programming. However, a common bottleneck occurs during training: as models improve, they quickly master the easy questions in a dataset. These solved problems become uninformative for the model's growth, while generating high-quality new data with human oversight remains a slow and expensive process.

Researchers at Tencent Hunyuan have developed a clever workaround called Composition-RL. Instead of searching for entirely new data, the system automatically stitches together multiple existing problems to create more difficult, compositional prompts. By forcing the model to navigate several sub-problems within a single query, the training process remains challenging and productive even after the original, simpler questions no longer offer a learning benefit.

The team also introduced a curriculum-based approach, which functions like a digital lesson plan that starts with simple combinations and gradually increases complexity as the model gets smarter. Their results demonstrate that this method consistently improves reasoning capabilities across various model sizes, specifically highlighting a significant trend in AI research: shifting focus from simply collecting more data to maximizing the utility of information we already possess.

Reinforcement learning for language models usually relies on verifiable rewards, where a system can objectively check if an answer is correct, such as in mathematics or computer programming. However, a common bottleneck occurs during training: as models improve, they quickly master the easy questions in a dataset. These solved problems become uninformative for the model's growth, while generating high-quality new data with human oversight remains a slow and expensive process.

Researchers at Tencent Hunyuan have developed a clever workaround called Composition-RL. Instead of searching for entirely new data, the system automatically stitches together multiple existing problems to create more difficult, compositional prompts. By forcing the model to navigate several sub-problems within a single query, the training process remains challenging and productive even after the original, simpler questions no longer offer a learning benefit.

The team also introduced a curriculum-based approach, which functions like a digital lesson plan that starts with simple combinations and gradually increases complexity as the model gets smarter. Their results demonstrate that this method consistently improves reasoning capabilities across various model sizes, specifically highlighting a significant trend in AI research: shifting focus from simply collecting more data to maximizing the utility of information we already possess.