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Re:DeepSeek-R1: Incentivizing Reasoning Capability in LLMs v... by gregoriol on Sunday January 26, 2025 @06:04AM (#42829318)
"recently trained" can't be an argument: those tools have to work with "current" data, otherwise they are useless.
Re:DeepSeek-R1: Incentivizing Reasoning Capability in LLMs v... by tomrod on Sunday January 26, 2025 @08:50AM (#42830094)
That's a different part of the implementation details. If you were to break the system into mocroservices, the model is a binary blob with a mocroservices wrapper and accessing web search is another microservice entirely. You really don't want the entire web to be constantly compressed and re-released as a new model iteration, it's super inefficient.
Re:DeepSeek-R1: Incentivizing Reasoning Capability in LLMs v... by nailer on Monday January 27, 2025 @12:47AM (#42837826)
Technically you’re correct, but from a product point of view one should be able to get answers beyond the cut-off date. The current product fails to realise that some queries like “who is the current president of the USA” are time based and may need a search rather than an excuse.
Re:DeepSeek-R1: Incentivizing Reasoning Capability in LLMs v... by kemiller on Monday January 27, 2025 @11:16AM (#42842661)
This only holds water if they are able to retrain frequently, which they haven't demonstrated yet. But if they are as efficient as they seem, then maybe.