Exploring DeepSeek-R1 s Agentic Capabilities Through Code Actions

I ran a quick experiment investigating how DeepSeek-R1 carries out on agentic jobs, despite not supporting tool usage natively, and I was rather satisfied by preliminary results. This experiment runs DeepSeek-R1 in a single-agent setup, where the design not just prepares the actions but also creates the actions as executable Python code. On a subset1 of the GAIA validation split, DeepSeek-R1 outperforms Claude 3.5 Sonnet by 12.5% outright, bphomesteading.com from 53.1% to 65.6% correct, and other designs by an even larger margin:


The experiment followed model usage standards from the DeepSeek-R1 paper and pipewiki.org the model card: Don't use few-shot examples, prevent adding a system timely, and set the temperature level to 0.5 - 0.7 (0.6 was utilized). You can find further assessment details here.


Approach


DeepSeek-R1's strong coding abilities allow it to function as a representative without being clearly trained for tool use. By permitting the model to generate actions as Python code, it can flexibly communicate with environments through .


Tools are implemented as Python code that is included straight in the timely. This can be a basic function meaning or a module of a bigger plan - any legitimate Python code. The design then creates code actions that call these tools.


Arise from carrying out these actions feed back to the model as follow-up messages, driving the next steps till a final answer is reached. The agent structure is a basic iterative coding loop that moderates the discussion in between the model and its environment.


Conversations


DeepSeek-R1 is used as chat design in my experiment, where the model autonomously pulls extra context from its environment by utilizing tools e.g. by utilizing a search engine or fetching data from websites. This drives the conversation with the environment that continues up until a last answer is reached.


In contrast, o1 models are understood to carry out improperly when used as chat designs i.e. they don't attempt to pull context during a conversation. According to the connected post, o1 designs carry out best when they have the complete context available, with clear guidelines on what to do with it.


Initially, I also attempted a complete context in a single prompt technique at each action (with outcomes from previous actions consisted of), but this resulted in substantially lower scores on the GAIA subset. Switching to the conversational method explained above, I was able to reach the reported 65.6% performance.


This raises an intriguing question about the claim that o1 isn't a chat design - maybe this observation was more relevant to older o1 models that did not have tool use abilities? After all, isn't tool usage support a crucial system for enabling models to pull extra context from their environment? This conversational method certainly seems reliable for setiathome.berkeley.edu DeepSeek-R1, though I still require to conduct comparable try outs o1 designs.


Generalization


Although DeepSeek-R1 was mainly trained with RL on math and coding tasks, it is amazing that generalization to agentic tasks with tool use through code actions works so well. This capability to generalize to agentic jobs advises of current research study by DeepMind that reveals that RL generalizes whereas SFT remembers, although generalization to tool use wasn't examined in that work.


Despite its capability to generalize to tool usage, DeepSeek-R1 typically produces really long reasoning traces at each step, compared to other designs in my experiments, restricting the usefulness of this model in a single-agent setup. Even easier jobs in some cases take a long period of time to finish. Further RL on agentic tool use, be it through code actions or not, might be one option to improve efficiency.


Underthinking


I likewise observed the underthinking phenomon with DeepSeek-R1. This is when a thinking model often changes between different reasoning ideas without sufficiently checking out appealing paths to reach a correct option. This was a major reason for overly long reasoning traces produced by DeepSeek-R1. This can be seen in the tape-recorded traces that are available for download.


Future experiments


Another typical application of thinking models is to use them for preparing only, while using other designs for creating code actions. This could be a possible new function of freeact, if this separation of functions proves helpful for more complex jobs.


I'm also curious about how thinking designs that currently support tool use (like o1, o3, ...) carry out in a single-agent setup, with and without producing code actions. Recent developments like OpenAI's Deep Research or Hugging Face's open-source Deep Research, which likewise uses code actions, dokuwiki.stream look intriguing.