I ran a fast experiment examining how DeepSeek-R1 performs on agentic jobs, despite not supporting tool usage natively, and I was quite satisfied by preliminary outcomes. This experiment runs DeepSeek-R1 in a single-agent setup, where the design not just plans the actions however 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% absolute, from 53.1% to 65.6% appropriate, and other designs by an even bigger margin:
The experiment followed model usage standards from the DeepSeek-R1 paper and the model card: Don't utilize few-shot examples, prevent adding a system timely, and clashofcryptos.trade set the temperature to 0.5 - 0.7 (0.6 was utilized). You can discover more assessment details here.
Approach
DeepSeek-R1's strong coding abilities enable it to function as a representative without being clearly trained for tool usage. By permitting the design to create actions as Python code, it can flexibly communicate with environments through code execution.
Tools are executed as Python code that is included straight in the timely. This can be a simple function definition or a module of a bigger bundle - any legitimate Python code. The model then generates code actions that call these tools.
Results from performing these actions feed back to the model as follow-up messages, driving the next steps until a last answer is reached. The agent framework is a simple iterative coding loop that mediates the discussion in between the design and its environment.
Conversations
DeepSeek-R1 is used as chat model in my experiment, where the design autonomously pulls additional context from its environment by utilizing tools e.g. by utilizing an online search engine or bring information from websites. This drives the conversation with the environment that continues until a final response is reached.
In contrast, o1 designs are known to perform poorly when utilized as chat designs i.e. they do not try to pull context throughout a conversation. According to the connected article, o1 designs carry out best when they have the full context available, with clear instructions on what to do with it.
Initially, I likewise tried a full context in a single timely approach at each action (with arise from previous actions included), but this resulted in substantially 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 concern about the claim that o1 isn't a chat design - maybe this observation was more to older o1 designs that did not have tool usage abilities? After all, isn't tool use support a crucial mechanism for enabling models to pull additional context from their environment? This conversational approach certainly appears reliable for DeepSeek-R1, though I still need to perform similar try outs o1 models.
Generalization
Although DeepSeek-R1 was mainly trained with RL on mathematics and coding tasks, it is amazing that generalization to agentic tasks with tool usage via code actions works so well. This ability to generalize to agentic jobs reminds of current research by DeepMind that reveals that RL generalizes whereas SFT memorizes, although generalization to tool usage wasn't investigated because work.
Despite its ability to generalize to tool use, DeepSeek-R1 frequently produces long thinking traces at each action, compared to other designs in my experiments, restricting the effectiveness of this model in a single-agent setup. Even easier jobs often take a long period of time to finish. Further RL on agentic tool use, be it via code actions or not, could be one option to improve effectiveness.
Underthinking
I likewise observed the underthinking phenomon with DeepSeek-R1. This is when a thinking model often changes in between different reasoning ideas without adequately exploring promising paths to reach a correct option. This was a significant factor for extremely long thinking traces produced by DeepSeek-R1. This can be seen in the tape-recorded traces that are available for download.
Future experiments
Another common application of thinking models is to use them for planning just, while using other models for generating code actions. This might be a prospective new feature of freeact, if this separation of functions proves beneficial for more complex tasks.
I'm likewise curious about how reasoning designs that currently support tool usage (like o1, o3, ...) carry out in a single-agent setup, forum.altaycoins.com with and without producing code actions. Recent advancements like OpenAI's Deep Research or Hugging Face's open-source Deep Research, which likewise utilizes code actions, look fascinating.
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Exploring DeepSeek R1's Agentic Capabilities Through Code Actions
Abdul Dieter edited this page 3 months ago