A model-free RL method arbitrates between a functional baseline policy and a learning policy, transferring agency over time to yield a standalone policy with high goal-reaching rates and competitive returns on continuous-control tasks.
Conservative safety critics for exploration
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Safe-Support Q-Learning trains Q-functions and policies in reinforcement learning without ever visiting unsafe states by constraining the behavior policy to a safe set and using KL-regularized Bellman targets in a two-stage framework.
Pre-VLA is a multimodal runtime verifier that predicts safety confidence and advantage scores for action chunks, raising closed-loop success rates on the LIBERO benchmark from 30.79% to 37.62%.
citing papers explorer
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An Agency-Transferring Model-Free Policy Enhancement Technique
A model-free RL method arbitrates between a functional baseline policy and a learning policy, transferring agency over time to yield a standalone policy with high goal-reaching rates and competitive returns on continuous-control tasks.
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Safe-Support Q-Learning: Learning without Unsafe Exploration
Safe-Support Q-Learning trains Q-functions and policies in reinforcement learning without ever visiting unsafe states by constraining the behavior policy to a safe set and using KL-regularized Bellman targets in a two-stage framework.
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Pre-VLA: Preemptive Runtime Verification for Reliable Vision-Language-Action and World-Model Rollouts
Pre-VLA is a multimodal runtime verifier that predicts safety confidence and advantage scores for action chunks, raising closed-loop success rates on the LIBERO benchmark from 30.79% to 37.62%.