sri

@citsecurities, research @togethercompute // prev @apple, research @nvidia

[NVIDIA AI Infrastructure @NVIDIAAIInfra]

That's a lot of daily tokens @togethercompute 👀

[sri @srivyanamandra]

@vipulved @togethercompute @nikogallogly @nytimes let’s go!!! proud and honored :)

[Vipul Ved Prakash @vipulved]

We @togethercompute believe intelligence should be abundant, not expensive.

Today we announced our Series C funding of $800m @ $8.3B valuation, to continue to build the world's most efficient platform for generative AI.

Thanks @nikogallogly for telling our story in @nytimes!

https://t.co/LGQ5t6rzya

[Together AI @togethercompute]

New from Together Research: Aurora.

Speculative decoding that adapts to shifting traffic in real time — and keeps improving the longer it runs.

Open-source, RL-based, 1.25x faster vs. a well-trained static speculator with no offline retraining pipeline.

Thread 🧵

[sri @srivyanamandra]

@appliedcompute congrats!

[Yulu Gan @yule_gan]

Reinforcement Learning (RL) has long been the dominant method for fine-tuning, powering many state-of-the-art LLMs. Methods like PPO and GRPO explore in action space. But can we instead explore directly in parameter space? YES we can. We propose a scalable framework for full-parameter fine-tuning using Evolution Strategies (ES).
By skipping gradients and optimizing directly in parameter space, ES achieves more accurate, efficient, and stable fine-tuning.
Paper: https://t.co/Es44ZqfcJ6
Code: https://t.co/eduztHwrLS

[Thinking Machines @thinkymachines]

LoRA makes fine-tuning more accessible, but it's unclear how it compares to full fine-tuning. We find that the performance often matches closely---more often than you might expect. In our latest Connectionism post, we share our experimental results and recommendations for LoRA.

https://t.co/fYV4FPi71m

[Thomas Ahle @thomasahle]

Deepmind won the moment LLMs became about RL.

[Randall Munroe @xkcd]

Path Minimization https://t.co/5SmWwustFJ