Data synthesis has become a crucial research area in large language models (LLMs), especially for generating high-quality instruction fine-tuning data to enhance downstream performance. In code generation, a key application of LLMs, manual annotation of code instruction data is costly. Recent methods, such as Code Evol-Instruct and OSS-Instruct, leverage LLMs to synthesize large-scale code instruction data, significantly improving LLM coding capabilities. However, these approaches face limitations due to unidirectional synthesis and randomness-driven generation, which restrict data quality and diversity. To overcome these challenges, we introduce Tree-of-Evolution (ToE), a novel framework that models code instruction synthesis process with a tree structure, exploring multiple evolutionary paths to alleviate the constraints of unidirectional generation. Additionally, we propose optimization-driven evolution, which refines each generation step based on the quality of the previous iteration. Experimental results across five widely-used coding benchmarks—HumanEval, MBPP, EvalPlus, LiveCodeBench, and BigCodeBench—demonstrate that base models fine-tuned on just 75k data synthesized by our method achieve comparable or superior performance to the state-of-the-art open-weight Code LLM, Qwen2.5-Coder-Instruct, which was fine-tuned on millions of samples.

Identifying the worries of individuals and societies plays a crucial role in providing social support and enhancing policy decision-making. Due to the popularity of social media platforms such as Twitter, users share worries about personal issues (e.g., health, finances, relationships) and broader issues (e.g., changes in society, environmental concerns, terrorism) freely. In this paper, we explore and evaluate a wide range of machine learning models to predict worry on Twitter. While this task has been closely associated with emotion prediction, we argue and show that identifying worry needs to be addressed as a separate task given the unique challenges associated with it. We conduct a user study to provide evidence that social media posts express two basic kinds of worry – normative and pathological – as stated in psychology literature. In addition, we show that existing emotion detection techniques underperform, especially while capturing normative worry. Finally, we discuss the current limitations of our approach and propose future applications of the worry identification system.