FlowCompile performs compile-time design space exploration on structured LLM workflows to produce reusable high-quality configuration sets that outperform routing baselines with up to 6.4x speedup.
arXiv preprint arXiv:2103.09568 , year=
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AET framework unifies SER and ESR criteria for nonlinear MORL; AETDICE enables offline optimization via DICE-style estimation in augmented state space.
An adaptive smooth Tchebycheff controller for multi-objective RL lets agents reach non-convex Pareto regions in robotic tasks while avoiding the instability of static non-linear scalarizations.
A single preference-conditioned policy achieves unique and Lipschitz-continuous Pareto coverage in multi-objective MDPs via a new mirror-descent policy iteration algorithm with O(1/k) convergence.
PRL-PUTS casts utility-weight tuning as a one-step value-based RL task and uses scalarization-parameter Pareto sweeping at inference time to generate and govern a family of policies, reporting +0.13% lift in successful sessions on Pinterest Homefeed.
citing papers explorer
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FlowCompile: An Optimizing Compiler for Structured LLM Workflows
FlowCompile performs compile-time design space exploration on structured LLM workflows to produce reusable high-quality configuration sets that outperform routing baselines with up to 6.4x speedup.
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AETDICE: Unified Framework and Offline Optimization for Nonlinear Multi-Objective RL
AET framework unifies SER and ESR criteria for nonlinear MORL; AETDICE enables offline optimization via DICE-style estimation in augmented state space.
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Adaptive Smooth Tchebycheff Attention for Multi-Objective Policy Optimization
An adaptive smooth Tchebycheff controller for multi-objective RL lets agents reach non-convex Pareto regions in robotic tasks while avoiding the instability of static non-linear scalarizations.
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A Single Deep Preference-Conditioned Policy for Learning Pareto Coverage Sets
A single preference-conditioned policy achieves unique and Lipschitz-continuous Pareto coverage in multi-objective MDPs via a new mirror-descent policy iteration algorithm with O(1/k) convergence.
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A Production-Ready RL Framework for Personalized Utility Tuning with Pareto Sweeping in Pinterest Recommender Systems
PRL-PUTS casts utility-weight tuning as a one-step value-based RL task and uses scalarization-parameter Pareto sweeping at inference time to generate and govern a family of policies, reporting +0.13% lift in successful sessions on Pinterest Homefeed.
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