  Augmented Language Models (ALMs) blend the reasoning capabilities of Large
Language Models (LLMs) with tools that allow for knowledge retrieval and action
execution. Existing ALM systems trigger LLM thought processes while pulling
observations from these tools in an interleaved fashion. Specifically, an LLM
reasons to call an external tool, gets halted to fetch the tool's response, and
then decides the next action based on all preceding response tokens. Such a
paradigm, though straightforward and easy to implement, often leads to huge
computation complexity from redundant prompts and repeated execution. This
study addresses such challenges for the first time, proposing a modular
paradigm ReWOO (Reasoning WithOut Observation) that detaches the reasoning
process from external observations, thus significantly reducing token
consumption. Comprehensive evaluations across six public NLP benchmarks and a
curated dataset reveal consistent performance enhancements with our proposed
methodology. Notably, ReWOO achieves 5x token efficiency and 4% accuracy
improvement on HotpotQA, a multi-step reasoning benchmark. Furthermore, ReWOO
demonstrates robustness under tool-failure scenarios. Beyond prompt efficiency,
decoupling parametric modules from non-parametric tool calls enables
instruction fine-tuning to offload LLMs into smaller language models, thus
substantially reducing model parameters. Our illustrative work offloads
reasoning ability from 175B GPT3.5 into 7B LLaMA, demonstrating the significant
potential for truly efficient and scalable ALM systems.
