Synthetic data augmentation helps channel-mixing time series models but degrades channel-independent ones, with reliable gains only from seasonal-trend generators and gradual schedules in low-resource settings.
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine
8 Pith papers cite this work. Polarity classification is still indexing.
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BRRL derives an analytic optimal policy for regularized constrained RL that guarantees monotonic improvement and yields the BPO algorithm that matches or exceeds PPO.
Response times modeled as drift-diffusion processes enable consistent estimation of population-average preferences from heterogeneous anonymous binary choices.
ANO derives a robust policy optimizer from geometric principles that replaces clipping with a smooth redescending gradient, showing better performance and stability than PPO, SPO, and GRPO in MuJoCo, Atari, and RLHF experiments.
A DRL-based event-triggered controller for networked artificial pancreas systems uses blood glucose change rules to formulate control as a semi-Markov decision process, improving communication efficiency.
Agent Q integrates MCTS-guided search, self-critique, and off-policy DPO to train LLM agents that outperform behavior cloning and reinforced fine-tuning baselines in WebShop and achieve up to 95.4% success in real-world booking scenarios.
TOPPO reformulates PPO with critic balancing to address gradient ill-conditioning in multi-task RL and reports stronger mean and tail performance than SAC baselines on Meta-World+ using fewer parameters and steps.
RAFT aligns generative models by ranking samples with a reward model and fine-tuning only on the top-ranked outputs, reporting gains on reward scores and automated metrics for LLMs and diffusion models.
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Response Time Enhances Alignment with Heterogeneous Preferences
Response times modeled as drift-diffusion processes enable consistent estimation of population-average preferences from heterogeneous anonymous binary choices.
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TOPPO: Rethinking PPO for Multi-Task Reinforcement Learning with Critic Balancing
TOPPO reformulates PPO with critic balancing to address gradient ill-conditioning in multi-task RL and reports stronger mean and tail performance than SAC baselines on Meta-World+ using fewer parameters and steps.