Privileged Foresight Distillation distills the residual difference in action predictions with versus without future context into a current-only adapter, yielding consistent gains on LIBERO and RoboTwin benchmarks.
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VLA-AD distills 7B VLA teachers into 158M students using offline VLM semantic guidance on task phases and directions, matching teacher performance on LIBERO with 44x size reduction and 3.28x speedup.
SDAR gates on-policy self-distillation signals into RL training to stabilize and improve multi-turn LLM agent performance on ALFWorld, WebShop, and Search-QA.
Integrating DVS event data into InterFuser through token fusion yields a driving score of 77.2 and 100% route completion on CARLA benchmarks, indicating improved robustness in dynamic conditions.
Semantic rollout plus town-adversarial regularization raises zero-shot success in held-out CARLA towns to 36.6% and 85.6% versus matched DreamerV3 baselines.
citing papers explorer
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Privileged Foresight Distillation: Zero-Cost Future Correction for World Action Models
Privileged Foresight Distillation distills the residual difference in action predictions with versus without future context into a current-only adapter, yielding consistent gains on LIBERO and RoboTwin benchmarks.
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Offline Semantic Guidance for Efficient Vision-Language-Action Policy Distillation
VLA-AD distills 7B VLA teachers into 158M students using offline VLM semantic guidance on task phases and directions, matching teacher performance on LIBERO with 44x size reduction and 3.28x speedup.
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Self-Distilled Agentic Reinforcement Learning
SDAR gates on-policy self-distillation signals into RL training to stabilize and improve multi-turn LLM agent performance on ALFWorld, WebShop, and Search-QA.
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InterFuserDVS: Event-Enhanced Sensor Fusion for Safe RL-Based Decision Making
Integrating DVS event data into InterFuser through token fusion yields a driving score of 77.2 and 100% route completion on CARLA benchmarks, indicating improved robustness in dynamic conditions.
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Dreaming Across Towns: Semantic Rollout and Town-Adversarial Regularization for Zero-Shot Held-Out-Town Fixed-Route Driving in CARLA
Semantic rollout plus town-adversarial regularization raises zero-shot success in held-out CARLA towns to 36.6% and 85.6% versus matched DreamerV3 baselines.