Moving fast is essentially an expression of humility as you make frequent contact with reality

LLM RL optimizes for sequential reasoning

We also optimize over the reasoning strategy, incl parallel trains of thought, aggregation of parallel traces, & sequential reasoning

This allows the model to better explore & allocate compute at test time

https://t.co/DkTSllkmvp

@jubayer_hamid: The most capable reasoning systems in AI scale inference compute along several axes: sequential compute to think longer, parallel compute to sample many independent attempts, and aggregative compute to synthesize prior traces into a new improved one. But during training, we only optimize how models use sequential compute. This creates a fundamental mismatch between how we ultimately deploy these systems and how we train them, leaving much of search and synthesis unoptimized.

We introduce SPIRAL, an RL framework for making all inference-compute primitives end-to-end learnable: models learn to coordinate sequential, parallel, and aggregative reasoning using only the reward of the final output. Work with @ifdita_hasan (co-lead), @michaelyli_ , @oshaikh13 , @yoonholeee , @DorsaSadigh , @chelseabfinn , @noahdgoodman 🧵

Can we translate a rough sense for what to do + VLA prior into successful behavior?

Flow reversal steering:

1. runs the VLA's flow ODE backwards to back out noise that's closest to coarse traj
2. runs flow forwards to get closest good behavior

Paper: https://t.co/FUDqDEpeJq

@tangerinecoder: Generalist robot policies learn many useful skills, but struggle to select good behaviors for new tasks. To solve this, we introduce Flow Reversal Steering (FRS), a method to refine coarse semantic guidance into precise, in-distribution motions. https://t.co/uCR6KmoDo8

1/N https://t.co/akRrTOhBYt

How to Make $10K a Month With Claude

Fun fact: @woj_zaremba wanted to completely remove that paper from existence a year or so after. I forgot why though -- Woj?

@OfirPress: I started doing language modeling research in Jan 2016, using the LSTM from the "RNN Regularization" model by @woj_zaremba @OriolVinyalsML and @ilyasut, trying to improve its perplexity on Penn Treebank (1M toks). This led to the weight tying method, sometimes still used today.

I'm joining in a few minutes!

@MTSlive: GOVT RESTRICTS GPT | OPENAI IPO DELAYED | ANTHROPIC ECONOMIC DATA https://t.co/lNuMPbGGe3

The biggest risk in AI is concentration: of power, capabilities and economic wealth. Who can doubt it with trillion dollar companies and government now already controlling a massive part of it?

So we need more rebels and more rebel alliance like this one from @usv and friends. Beautiful message!

@nickgrossman: https://t.co/GBy2md7JxD

Grab the Open Engine guide: the copy-paste task record that makes one AI's work the next AI's job, with receipts

The best way to build a high pain tolerance is to remind yourself that the pain is temporary

How to Become an AI Expert

We just crossed $100M annual run-rate. I know many AI companies are capturing much more $$$ these days, but still proud of the milestone!

Maximizing short-term revenue has never been our priority. In fact, we're proud to manage to store and serve hundreds of petabytes of models and datasets while keeping HF free and open-source for 97% of our users. As a platform, we’re happy to hopefully create orders of magnitude more value for the community than what we capture. To me, that’s the very definition of a platform.

And it has helped us build one of the most loved platform in tech, with network effects, a defensible position and a sustainable business which is quite unique in AI.

Many many thanks to all the community members for building with us, we wouldn't be anywhere without you! Can’t wait for what’s next, especially as more companies start to see the value of open and local AI! Next milestone $1B?

Bender is here!