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Forget parallel corpora: CodePivot shows you can train a 7B model to beat behemoth LLMs at multilingual code transpilation by pivoting through Python and using a clever RL reward.
Autonomous driving policies that ace open-loop tests can still crash and burn in the real world, because they learn to ignore the reactive nature of closed-loop environments.
LLMs can now generate Verilog code that's not just correct, but also optimized for real-world hardware constraints like power, performance, and area, thanks to a novel multi-agent system with evolving memory.
