✨ Introducing ThreadWeaver 🧵⚡ — an approach that significantly reduces LLM reasoning latency on challenging problems by enabling models to adaptively spawn parallel reasoning threads and merge them later in the process. (An off-the-shelf reasoning LLM can be retrofitted to perform adaptive parallel reasoning with this approach, too!) ThreadWeaver was led by the amazing @LongTonyLian. It was developed based on the paradigm of adaptive parallel reasoning ( For the first time, we show that adaptive parallel reasoning can achieve accuracy comparable to equally sized cutting-edge sequential reasoning models (e.g., 79.9% for ThreadWeaver vs. 78.3% for Qwen3-8B on AIME24, and 71.9% vs. 72.2% on average across six math reasoning benchmarks) while delivering substantial reductions in token latency (1.14× speedup on AIME24 and up to 1.53× across datasets). ThreadWeaver was designed to be fully compatible with standard LLM inference engines that support text-completion APIs (i.e., it does not introduce architectural changes or modify context representation to support adaptive parallelization). The approach can be directly applied to retrofit off-the-shelf reasoning LLMs through supervised fine-tuning (inspired by Multiverse) and a novel on-policy reinforcement learning method incorporating thread-wise advantage broadcast and a parallelization-aware reward design. ThreadWeaver opens the door to a future where models can understand both the problem structure and execution environment well enough to adaptively leverage available resources in the most efficient way to solve complex tasks. This is also the last research project I contributed to during my time at Meta 👩‍💻 For more details, be sure to check out @LongTonyLian's thread: