DeepSeek-R1 Thoughtology: Let's <think> about LLM Reasoning
Authors: name: Sara Vera Marjanović, name: Arkil Patel, name: Vaibhav Adlakha, name: Milad Aghajohari, name: Parishad BehnamGhader, name: Mehar Bhatia, name: Aditi Khandelwal, name: Austin Kraft, name: Benno Krojer, name: Xing Han Lù, name: Nicholas Meade, name: Dongchan Shin, name: Amirhossein Kazemnejad, name: Gaurav Kamath, name: Marius Mosbach, name: Karolina Stańczak, name: Siva Reddy

Large Reasoning Models like DeepSeek-R1 mark a fundamental shift in how LLMs
approach complex problems. Instead of directly producing an answer for a given
input, DeepSeek-R1 creates detailed multi-step reasoning chains, seemingly
"thinking" about a problem before providing an answer. This reasoning process
is publicly available to the user, creating endless opportunities for studying
the reasoning behaviour of the model and opening up the field of Thoughtology.
Starting from a taxonomy of DeepSeek-R1's basic building blocks of reasoning,
our analyses on DeepSeek-R1 investigate the impact and controllability of
thought length, management of long or confusing contexts, cultural and safety
concerns, and the status of DeepSeek-R1 vis-\`a-vis cognitive phenomena, such
as human-like language processing and world modelling. Our findings paint a
nuanced picture. Notably, we show DeepSeek-R1 has a 'sweet spot' of reasoning,
where extra inference time can impair model performance. Furthermore, we find a
tendency for DeepSeek-R1 to persistently ruminate on previously explored
problem formulations, obstructing further exploration. We also note strong
safety vulnerabilities of DeepSeek-R1 compared to its non-reasoning
counterpart, which can also compromise safety-aligned LLMs.