Fireworks 发布 Ember-1，HN 讨论聚焦推理模型过度思考与 token 削减

Domain: fireworks.ai Points: 67 | Comments: 19 [Top Comments] - > The problem: thinking models think too much Analysis paralysis stifles not just human intelligence, but other intelligences too. - I don't think the article mentions Pareto frontier enough. Also, did I miss a memo? Suddenly every article on AI seems to be talking about the Pareto frontier - or have I just not been paying attention? - I guess they figure "best bang for your buck" comes off a little too colloquial. - Need this done for DeepSeek, ideally one of the Flash models. - If you have the compute, I have the expertise. - Well done, and great iteration. The pareto frontier needs clearer distinction. Benchmarks miss half the story. What, if any, capability is lost by the token reduction (for example, was it like super awesome at Golang before and now kind of sucks? that kind of distinction). - It looks like it would be similar to GLM 5.3 Flash, had they tested it... - Ignoring for the moment issues of what "counts" as open, won't open models rapidly advance due to stuff like this in ways that it's less possible for the proprietary ones to do? This is exactly how Linux & Wikipedia, for example, overtook their "frontiers", right? - Yes, absolutely, but only if people keep contributing in the open. - No, because close labs/models borrow but don't contribute back. - > Ignoring for the moment issues of what "counts" as open, won't open models rapidly advance due to stuff like this in ways that it's less possible for the proprietary ones to do? This is exactly how Linux & Wikipedia, for example, overtook their "frontiers", right? I suspect the advantage that catapulted Linux ahead of the establishment was less technical potential and talent and more organizational advantage. That's not to diminish the technical talent of the Linux crew, but them being unencumbered gave them more degrees of freedom. The rest is history. So as long as the AI companies don't succumb to "big company" dynamics, they can outlead. To wit: Open AI and Anthropic are kicking Google's ass. - On the smaller end, Quen 3.8, while being extraordinarily capable for a small local model, also suffers from extreme thinking. I wonder if the techniques described here generalize to other models too. - I suspect it might generalize to other large models, but I don't think Qwen3.8 27B is one of them. Kimi K3 is a 2.8 trillion parameter model, and I suspect that is playing a big role in being able to reduce the length of CoT without taking a hit in quality. That's just vibes, though. - Does anybody know if this would be a good model for creative writing? - So they trained a model on open weights, and then aren't releasing the weights... am I reading this right? - That's truly the least interesting aspect of what they've done. - Technically kimi k-3 weights license is not open weight (it has a lot of restrictions). I would classify it as ‘weight open’ similar to the bsl and fsl ’source open’ licenses. - Off topic:With sol pricing drop tbh kimi k3’s value prop has not been that great. For our internal use case/testing/benchmarks sol come out with way better quality and much cheaper costs. Kimi really needs to drop their pricing (I heard it’s set by them across all the neoclouds) Sol is at 2/10 vs kimi’s 3/15