I ran a quick experiment investigating how DeepSeek-R1 carries out on agentic jobs, despite not supporting tool usage natively, and I was quite amazed by initial outcomes. This experiment runs DeepSeek-R1 in a single-agent setup, where the design not only plans the actions but also creates the actions as executable Python code. On a subset1 of the GAIA recognition split, DeepSeek-R1 surpasses Claude 3.5 Sonnet by 12.5% outright, from 53.1% to 65.6% correct, and other models by an even bigger margin:
The experiment followed model use standards from the DeepSeek-R1 paper and the model card: Don't utilize few-shot examples, prevent including a system prompt, and set the temperature level to 0.5 - 0.7 (0.6 was utilized). You can find additional assessment details here.
Approach
DeepSeek-R1's strong coding abilities enable it to serve as a representative without being clearly trained for tool use. By enabling the model to generate actions as Python code, it can flexibly interact with environments through code execution.
Tools are carried out as Python code that is included straight in the prompt. This can be a simple function definition or a module of a larger package - any valid Python code. The model then creates code actions that call these tools.
Arise from performing these actions feed back to the design as follow-up messages, driving the next steps up until a final response is reached. The representative structure is a basic iterative coding loop that moderates the conversation between the model and its environment.
Conversations
DeepSeek-R1 is utilized as chat model in my experiment, where the model autonomously pulls additional context from its environment by utilizing tools e.g. by using an online search engine or fetching information from web pages. This drives the discussion with the environment that continues until a last answer is reached.
On the other hand, o1 designs are understood to perform poorly when utilized as chat designs i.e. they do not attempt to pull context during a conversation. According to the linked post, o1 designs carry out best when they have the full context available, with clear directions on what to do with it.
Initially, historydb.date I also tried a complete context in a single timely technique at each step (with outcomes from previous actions included), however this led to significantly lower ratings on the GAIA subset. Switching to the conversational approach explained above, I had the ability to reach the reported 65.6% performance.
This raises an intriguing question about the claim that o1 isn't a chat design - perhaps this observation was more appropriate to older o1 models that lacked tool usage capabilities? After all, isn't tool use support an important mechanism for enabling models to pull additional context from their environment? This conversational method certainly seems reliable for DeepSeek-R1, though I still require to conduct similar explores o1 designs.
Generalization
Although DeepSeek-R1 was mainly trained with RL on math and coding tasks, it is remarkable that generalization to agentic tasks with tool use through code actions works so well. This ability to generalize to agentic jobs advises of current research study by DeepMind that shows that RL generalizes whereas SFT memorizes, although generalization to tool use wasn't investigated because work.
Despite its ability to generalize to tool use, DeepSeek-R1 often produces long thinking traces at each step, compared to other designs in my experiments, restricting the of this model in a single-agent setup. Even easier jobs sometimes take a very long time to finish. Further RL on agentic tool use, be it by means of code actions or not, could be one alternative to enhance performance.
Underthinking
I likewise observed the underthinking phenomon with DeepSeek-R1. This is when a reasoning model regularly changes between different thinking thoughts without adequately checking out promising paths to reach an appropriate service. This was a major factor for extremely long reasoning traces produced by DeepSeek-R1. This can be seen in the recorded traces that are available for download.
Future experiments
Another common application of thinking designs is to use them for planning only, while utilizing other models for creating code actions. This might be a prospective new feature of freeact, if this separation of functions shows beneficial for more complex tasks.
I'm also curious about how reasoning designs that already support tool use (like o1, o3, ...) perform in a single-agent setup, with and without creating code actions. Recent advancements like OpenAI's Deep Research or Hugging Face's open-source Deep Research, which likewise utilizes code actions, look intriguing.
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Exploring DeepSeek-R1's Agentic Capabilities Through Code Actions
Aaron Barbosa edited this page 2025-02-11 00:34:37 +02:00