MAPL trains quadruped locomotion policies from LLM-generated multi-objective trajectory preferences and matches or exceeds expert-designed reward performance in four environments without manual reward engineering.
Surf: Semi-supervised reward learning with data augmentation for feedback- efficient preference-based reinforcement learning
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UNVERDICTED 5representative citing papers
Success Visitation Matching uses a discriminator to turn sparse outcome rewards into dense process rewards by matching visitations of successful episodes, provably preserving the optimal policy and speeding up robotic RL finetuning.
SENIOR improves feedback efficiency and policy learning speed in PbRL by combining motion-distinction query selection via kernel density estimation with preference-guided intrinsic rewards, showing gains on simulated and real robot tasks.
Entropy minimization on self-generated outputs elicits strong reasoning in pretrained LLMs, matching or exceeding supervised RL methods on benchmarks.
Themis is an XAI-enabled framework for RL from human feedback that supports 200+ environments and includes a scalable cloud platform for collecting human preferences.
citing papers explorer
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MAPL: Multi-Objective Preference Learning for Robot Locomotion
MAPL trains quadruped locomotion policies from LLM-generated multi-objective trajectory preferences and matches or exceeds expert-designed reward performance in four environments without manual reward engineering.
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Learning Process Rewards via Success Visitation Matching for Efficient RL
Success Visitation Matching uses a discriminator to turn sparse outcome rewards into dense process rewards by matching visitations of successful episodes, provably preserving the optimal policy and speeding up robotic RL finetuning.
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SENIOR: Efficient Query Selection and Preference-Guided Exploration in Preference-based Reinforcement Learning
SENIOR improves feedback efficiency and policy learning speed in PbRL by combining motion-distinction query selection via kernel density estimation with preference-guided intrinsic rewards, showing gains on simulated and real robot tasks.
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The Unreasonable Effectiveness of Entropy Minimization in LLM Reasoning
Entropy minimization on self-generated outputs elicits strong reasoning in pretrained LLMs, matching or exceeding supervised RL methods on benchmarks.
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Themis: An explainable AI-enabled framework for Reinforcement Learning with Human Feedback
Themis is an XAI-enabled framework for RL from human feedback that supports 200+ environments and includes a scalable cloud platform for collecting human preferences.