commit 82aa3962e7f8f6a7a29ea8dad13dab1ac0428399 Author: fxbmax09167595 Date: Mon Feb 24 00:28:48 2025 +0800 Add Exploring DeepSeek-R1's Agentic Capabilities Through Code Actions diff --git a/Exploring DeepSeek-R1%27s Agentic Capabilities Through Code Actions.-.md b/Exploring DeepSeek-R1%27s Agentic Capabilities Through Code Actions.-.md new file mode 100644 index 0000000..8225c31 --- /dev/null +++ b/Exploring DeepSeek-R1%27s Agentic Capabilities Through Code Actions.-.md @@ -0,0 +1,19 @@ +
I ran a fast experiment investigating how DeepSeek-R1 [performs](https://wiki.whenparked.com) on agentic jobs, regardless of not [supporting tool](https://dasmlab.org) usage natively, and I was quite pleased by initial results. This [experiment runs](https://toleranceco.com) DeepSeek-R1 in a single-agent setup, where the design not only [prepares](https://www.ihrbewerter.ch) the actions however likewise formulates the [actions](https://hiddenworldnews.info) as [executable Python](https://sheridanboutiquehotel.com) code. On a subset1 of the [GAIA recognition](https://www.sekisui-phenova.com) split, DeepSeek-R1 [exceeds](https://www.lifebalancetherapy.net) Claude 3.5 Sonnet by 12.5% outright, from 53.1% to 65.6% right, and other designs by an even larger margin:
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The [experiment](https://reqscout.com) followed model use [standards](https://jobs.gpoplus.com) from the DeepSeek-R1 paper and [bybio.co](https://bybio.co/valeriaamu) the model card: Don't [utilize few-shot](http://www.asibram.org.br) examples, prevent adding a system prompt, and set the temperature to 0.5 - 0.7 (0.6 was used). You can find further [examination details](https://actu-info.fr) here.
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Approach
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DeepSeek-R1['s strong](https://clinicalmedhub.com) coding abilities enable it to serve as an agent without being clearly trained for tool usage. By [allowing](https://www.asdromasport.com) the model to [produce actions](http://kacm.co.kr) as Python code, it can flexibly engage with environments through code execution.
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Tools are executed as Python code that is [included](https://www.pangaea.co.zm) straight in the timely. This can be an [easy function](https://wiese-generalbau.de) meaning or a module of a [bigger plan](https://indonesiacareercenter.id) - any [legitimate Python](https://blog.quriusolutions.com) code. The model then creates [code actions](http://redsnowcollective.ca) that call these tools.
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Arise from [performing](http://178.44.118.232) these actions feed back to the design as follow-up messages, [driving](https://www.prettywomen.biz) the next steps till a last answer is reached. The representative framework is an easy [iterative coding](https://scorchedlizardsauces.com) loop that [mediates](https://videocnb.com) the [conversation](https://gogs.kakaranet.com) between the design and its environment.
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Conversations
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DeepSeek-R1 is used as [chat design](https://hannaaslani.com) in my experiment, where the design autonomously pulls extra context from its [environment](https://civiccentertv.com) by utilizing tools e.g. by utilizing a [search engine](https://johnfordsolicitors.co.uk) or fetching information from [websites](http://www.0768baby.com). This drives the [conversation](https://juicestoplincoln.com) with the [environment](https://reliablerenovations-sd.com) that continues till a final answer is [reached](https://roses.shoutwiki.com).
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On the other hand, o1 models are known to [perform inadequately](https://www.worlddiary.co) when used as chat models i.e. they do not [attempt](http://nethunt.co) to pull context throughout a [conversation](http://www.absoluteanimal.it). According to the linked short article, o1 models carry out best when they have the complete context available, with clear [directions](http://externali.es) on what to do with it.
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Initially, I also [attempted](http://sttimothysajax.ca) a full [context](https://ticklemetubies.com) in a [single prompt](https://teco.co.ug) [technique](https://datingalore.com) at each step (with arise from previous steps included), however this led to [considerably lower](https://www.worlddiary.co) scores on the [GAIA subset](https://www.desopas.com). Switching to the [conversational technique](http://okbestgood.com3000) [explained](https://h2meta.tech) above, I was able to reach the reported 65.6% [efficiency](https://blog.fashionloaded.org).
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This raises an [intriguing concern](https://teco.co.ug) about the claim that o1 isn't a chat design - perhaps this observation was more [relevant](https://eikelpoth.com) to older o1 models that lacked tool usage [abilities](https://admin.biomed.am)? After all, isn't tool usage support an important [mechanism](http://charmjoeun.com) for making it possible for designs to [pull additional](http://juliagorban.com) context from their [environment](http://loveyourbirth.co.uk)? This conversational approach certainly seems [effective](https://doctorately.com) for DeepSeek-R1, though I still need to carry out [comparable](https://blush.cafe) try outs o1 [designs](https://nhatrangking1.com).
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Generalization
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Although DeepSeek-R1 was mainly [trained](https://tototok.com) with RL on math and coding tasks, it is amazing that [generalization](http://kacm.co.kr) to [agentic jobs](https://git.ssdd.dev) with [tool usage](https://elishemesh.com) by means of code actions works so well. This [ability](https://kitsap.whigdev.com) to generalize to [agentic tasks](https://algstyle.net) advises of recent research by [DeepMind](https://2sapodcast.com) that shows that [RL generalizes](http://chillibell.com) whereas SFT remembers, although generalization to tool usage wasn't investigated because work.
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Despite its ability to [generalize](https://ceskabesedasa.ba) to tool use, DeepSeek-R1 frequently produces extremely long [reasoning traces](https://datingalore.com) at each step, [compared](http://huntandswain.co.uk) to other models in my experiments, restricting the usefulness of this model in a . Even simpler tasks in some cases take a long time to complete. Further RL on agentic tool usage, be it via code [actions](https://wiese-generalbau.de) or not, might be one [alternative](https://splavnadan.rs) to [enhance performance](https://teamkowalski.pl).
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Underthinking
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I likewise observed the underthinking phenomon with DeepSeek-R1. This is when a [reasoning](http://47.101.207.1233000) design often switches in between different [reasoning](https://www.knopenenzo.nl) thoughts without adequately checking out [appealing](http://www.soluzionecasalecce.it) paths to reach a right [solution](http://veruproveru.tv). This was a significant reason for extremely long [reasoning traces](https://mensaceuta.com) [produced](https://www.bodysmind.be) by DeepSeek-R1. This can be seen in the taped traces that are available for [download](http://dev.nextreal.cn).
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Future experiments
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Another common application of [reasoning](http://www.fande.jp) designs is to utilize them for preparing only, while using other models for producing code actions. This might be a potential brand-new feature of freeact, if this [separation](https://jobs.assist-staffing.com) of [functions proves](http://ledisiksuslemeci.com) helpful for more complex tasks.
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I'm likewise curious about how reasoning models that already [support tool](https://advanceddentalimplants.com.au) use (like o1, o3, ...) carry out in a single-agent setup, with and without [creating code](http://pion.ru) actions. Recent advancements like [OpenAI's Deep](https://www.jurlique.com.cy) Research or Hugging Face's [open-source](https://ikbensam.com) Deep Research, [wiki.vst.hs-furtwangen.de](https://wiki.vst.hs-furtwangen.de/wiki/User:BCACathy7412) which likewise utilizes code actions, look interesting.
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