Reinforcement learning at
@cognition's scale is a hard infrastructure problem. We are proud to be part of the stack behind it.
Congrats to the team on SWE-2!
Read more about how we think about RL at Fireworks:
Introducing SWE-2, our closest model yet to the frontier.
On leading evals, it scores on par with recent frontier models – at up to 70% lower cost.
We scaled RL to multiple trillions of parameters, with a refined recipe that pushes the Pareto curve on both capabilities & cost.
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