Row 8545

Row ID: 8545 | Dataset Entry | Axioma AXP Content Repository

Content Data

This page contains data entry 8545 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

I was recently revisiting OpenAI’s paper on [DOTA2 Open Five](https://cdn.openai.com/dota-2.pdf), and it’s so impressive what they did there from both engineering and research standpoint. Creating a distributed system of 50k CPUs for the rollout, 1k GPUs for training while taking between 8k and 80k actions from 16k observations per 0.25s—how crazy is that?? They also were doing “surgeries” on the RL model to recover weights as their reward function, observation space, and even architecture has changed over the couple months of training. Last but not least, they beat the OG team (world champions at the time) and deployed the agent to play live with other players online.

Fast forward a couple of years, they are predicting the next token in a sequence. Don’t get me wrong, the capabilities of gpt4 and its omni version are truly amazing feat of engineering and research (probably much more useful), but they don’t seem to be as interesting (from the research perspective) as some of their previous work.

So, now I am wondering how did the engineers and researchers transition throughout the years? Was it mostly due to their financial situation and need to become profitable or is there a deeper reason for their transition?

FieldValue
text I was recently revisiting OpenAI’s paper on [DOTA2 Open Five](https://cdn.openai.com/dota-2.pdf), and it’s so impressive what they did there from both engineering and research standpoint. Creating a distributed system of 50k CPUs for the rollout, 1k GPUs for training while taking between 8k and 80k actions from 16k observations per 0.25s—how crazy is that?? They also were doing “surgeries” on the RL model to recover weights as their reward function, observation space, and even architecture has c…
label r/machinelearning
dataType post
communityName r/MachineLearning
datetime 2024-05-19
username_encoded Z0FBQUFBQm5Lakw0WlFFbFg3c1RxdUZuRVhkS2w2a1BwZjQ3cjhaUTVUQ2U0bDBWMEhhVE1zMEJTWXgtcDJobHlGR3ktclU0ZFNVOTdzTWFiMmlNbER0QTJpbDRfWlp1SEE9PQ==
url_encoded Z0FBQUFBQm5Lak9IdGtoN1czbTEwWUNteGtiRGptYmNLN0k4S3VWcmlJbmdIZ0psdWNDaGJDTWlWVnRnYUtsMFA3bUlfTnJaRDdrZmg4bWhJXzlwY2FmWVVRai1GR1haQ01LSjdSeFJEVkhHU2FMYUZCUGVKOFR6VGR1RlBfOEp4V2FINU40QXh1U21ocDZ0bzllZTlyRUZTemV0R1hROXZkY2FqdmdEZUx3RFo0eGVQU2J3NG9Ebk5YUS1iZC16VV9udFAwbWlqbnN0MHJEYzlnMi00QTU1X1NPbkdpSU5zQT09

Raw Record

{
  "text": "I was recently revisiting OpenAI’s paper on [DOTA2 Open Five](https://cdn.openai.com/dota-2.pdf), and it’s so impressive what they did there from both engineering and research standpoint. Creating a distributed system of 50k CPUs for the rollout, 1k GPUs for training while taking between 8k and 80k actions from 16k observations per 0.25s—how crazy is that?? They also were doing “surgeries” on the RL model to recover weights as their reward function, observation space, and even architecture has changed over the couple months of training. Last but not least, they beat the OG team (world champions at the time) and deployed the agent to play live with other players online. \n\nFast forward a couple of years, they are predicting the next token in a sequence. Don’t get me wrong, the capabilities of gpt4 and its omni version are truly amazing feat of engineering and research (probably much more useful), but they don’t seem to be as interesting (from the research perspective) as some of their previous work.\n\nSo, now I am wondering how did the engineers and researchers transition throughout the years? Was it mostly due to their financial situation and need to become profitable or is there a deeper reason for their transition?\n",
  "label": "r/machinelearning",
  "dataType": "post",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-19",
  "username_encoded": "Z0FBQUFBQm5Lakw0WlFFbFg3c1RxdUZuRVhkS2w2a1BwZjQ3cjhaUTVUQ2U0bDBWMEhhVE1zMEJTWXgtcDJobHlGR3ktclU0ZFNVOTdzTWFiMmlNbER0QTJpbDRfWlp1SEE9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak9IdGtoN1czbTEwWUNteGtiRGptYmNLN0k4S3VWcmlJbmdIZ0psdWNDaGJDTWlWVnRnYUtsMFA3bUlfTnJaRDdrZmg4bWhJXzlwY2FmWVVRai1GR1haQ01LSjdSeFJEVkhHU2FMYUZCUGVKOFR6VGR1RlBfOEp4V2FINU40QXh1U21ocDZ0bzllZTlyRUZTemV0R1hROXZkY2FqdmdEZUx3RFo0eGVQU2J3NG9Ebk5YUS1iZC16VV9udFAwbWlqbnN0MHJEYzlnMi00QTU1X1NPbkdpSU5zQT09"
}
Explore Dataset Explore Row

Entry Information