59.76% on AIME is really appealing. Without having had time to understand it and determine whether it's useful or not, I see this number as indicating that this could be a stepping stone on something like the o1-to-DeepSeek-R1 progression for thinking, where open source models eventually figured out how o1 worked, only for the less definite 'o1' and instead what Google achieved and OpenAI may have achieved on the 2025 IMO problems.
I stumbled across this AI paper just now. It sounds intimidatingly technical, but if you read the abstract and look at Figures 1 and 2 and Equation 6, I think it's got some neat and accessible conceptual ideas.
Supervised learning is a much more mature technology than reinforcement learning, so it seems like a good thing to leverage that.
59.76% on AIME is really appealing. Without having had time to understand it and determine whether it's useful or not, I see this number as indicating that this could be a stepping stone on something like the o1-to-DeepSeek-R1 progression for thinking, where open source models eventually figured out how o1 worked, only for the less definite 'o1' and instead what Google achieved and OpenAI may have achieved on the 2025 IMO problems.
I stumbled across this AI paper just now. It sounds intimidatingly technical, but if you read the abstract and look at Figures 1 and 2 and Equation 6, I think it's got some neat and accessible conceptual ideas.
Supervised learning is a much more mature technology than reinforcement learning, so it seems like a good thing to leverage that.
Is this DPO?
I think you meant to link to
Implicit Actor Critic Coupling via a Supervised Learning Framework for RLVR https://arxiv.org/abs/2509.02522
not
Winning Gold at IMO 2025 with a Model-Agnostic Verification-and-Refinement Pipeline https://arxiv.org/abs/2507.15855
We've changed the top link to that from https://arxiv.org/abs/2507.15855. Thanks!
Ack, thank you.