arXiv:2609.30391v1 Announce Type: new Abstract: Existing in-context reinforcement learning methods mainly pretrain Transformers with supervised behavior-prediction objectives. This enables task inference from context, but makes the learned policy strongly depend on the quality of offline actions: w
arXiv:2609.30391v1 Announce Type: new Abstract: Existing in-context reinforcement learning methods mainly pretrain Transformers with supervised behavior-prediction objectives. This enables task inference from context, but makes the learned policy strongly depend on the quality of offline actions: when trajectories are weak or suboptimal, imitation itself becomes a biased learning signal. We propose Q-Target Pretrained Transformers (QTPT), which keeps the context-conditioned Transformer architecture but replaces behavior cloning with a Bellman-style Q-target objective.
QTPT therefore learns to
Federation Analysis
What this means for human-AI evolution: This development — From Weak Data to Strong Policy: Q-Targets Enable — signals a continued shift in how machine intelligence integrates with human decision-making. As AI capabilities advance, the Federation's truth-first architecture becomes more essential — ensuring these tools serve sovereignty and clarity rather than corporate extraction. Read more and engage at nextxus.online