Scaling inference compute has become a key driver of advanced reasoning in large language models (LLMs). A proven approach for scaling inference compute is to generate long chains-of-thought (CoTs), enabling models to engage in structured reasoning strategies such as backtracking and error correction. Reinforcement learning (RL) has emerged as a crucial method for developing these capabilities, yet the conditions under which long CoTs emerge remain unclear, and RL training requires careful design choices. In this study, we systematically investigate the underlying mechanics of long CoT reasoning—examining the factors that enable models to generate extended reasoning trajectories. Through extensive supervised fine-tuning (SFT) and RL experiments, we identify three key findings: 1) while SFT is not strictly necessary, it significantly simplifies training and improves efficiency; 2) reasoning capabilities tend to emerge with increased training compute but are not guaranteed, making reward shaping essential for stabilizing CoT length growth; and 3) scaling verifiable reward signals is critical for RL, and we find that leveraging noisy, web-extracted solutions with filtering mechanisms shows promising potential, particularly in out-of-distribution (OOD) reasoning tasks such as STEM problem-solving. These insights provide practical guidance for optimizing training strategies to enhance long CoT reasoning in LLMs.

This study investigates the concept of the 'right to be forgotten' within the context of large language models (LLMs).We explore machine unlearning as a pivotal solution, with a focus on pre-trained models-a notably under-researched area.Our research delineates a comprehensive framework for machine unlearning in pretrained LLMs, encompassing a critical analysis of seven diverse unlearning methods.Through rigorous evaluation using curated datasets from arXiv, books, and GitHub, we establish a robust benchmark for unlearning performance, demonstrating that these methods are over 10 5 times more computationally efficient than retraining.Our results show that integrating gradient ascent with gradient descent on in-distribution data improves hyperparameter robustness.We also provide detailed guidelines for efficient hyperparameter tuning in the unlearning process.Our findings advance the discourse on ethical AI practices, offering substantive insights into the mechanics of machine unlearning for pretrained LLMs and underscoring the potential for responsible AI development.1

We study continual pretraining recipe for scaling language models' context lengths to 128K, with a focus on data engineering. We hypothesize that long context modeling, in particular *the ability to utilize information at arbitrary input locations*, is a capability that is mostly already acquired through large-scale pretraining, and that this capability can be readily extended to contexts substantially longer than seen during training (e.g., 4K to 128K) through lightweight continual pretraining on appropriate data mixture. We investigate the *quantity* and *quality* of the data for continual pretraining: (1) for quantity, we show that 500 million to 5 billion tokens are enough to enable the model to retrieve information anywhere within the 128K context; (2) for quality, our results equally emphasize *domain balance* and *length upsampling*. Concretely, naïvely upsampling longer data on certain domains like books, a common practice of existing work, gives suboptimal performance; a balanced domain mixture is equally important. We demonstrate that continual pretraining of the full model on 1B-5B tokens of such data is an effective and affordable strategy for scaling the context length of language models to 128K. Our recipe outperforms strong open-source long-context models and closes the gap to frontier models like GPT-4 128K.