The authors extend generative quantum eigensolver to produce circuits with upper-bounded quantum circuit-cutting overhead for molecular ground-state search, tested via transformer decoder on BeH2 with a new loss function and hybrid training strategy.
Contrastive preference optimization: Pushing the boundaries of llm performance in machine translation
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A survey deriving a unified policy gradient framework for LLM post-training methods and providing technical comparisons of PPO, GRPO, DPO variants.
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Generative quantum eigensolver with constrained circuit-cutting overhead
The authors extend generative quantum eigensolver to produce circuits with upper-bounded quantum circuit-cutting overhead for molecular ground-state search, tested via transformer decoder on BeH2 with a new loss function and hybrid training strategy.
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Reinforcement Learning for LLM Post-Training: A Survey
A survey deriving a unified policy gradient framework for LLM post-training methods and providing technical comparisons of PPO, GRPO, DPO variants.