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GenCTL is proposed, a prompt-based framework for effective and model-aware NL2CTL translation without task-specific fine-tuning that improves the reliability and practical checkability of LLM-generated CTL specifications.
An angular effective learning rate is derived that accounts for the parameter-update angle, parameter norm, and update norm, and shows that the conventional norm-based measure is a special case under parameter-update orthogonality.
It is found that OPD transfers a teacher's reasoning behavior rather than its answers to particular problems: training difficulty barely matters, and even problems the teacher never solves are useful.
This work presents a unified black-box RL framework for stable and scalable optimization of general agents through complex harnesses, supporting unified training across heterogeneous execution systems.
This work presents LoopCoder pre-trained on 12T+ code and general tokens, along with LoopCoder-Thinking and LoopCoder-Instruct variants, the first large-scale looped transformer for code, achieving comparable performance to standard dense architectures with more parameters.
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