decision-transformer-rl-as-sequence-modeling

IN premise — entries/2026/06/21/wiki-Transformer_deep_learning_architecture-chunk-7.md

Created 2026-06-21T09:55:55+00:00

Decision Transformer (Chen et al., 2021) casts reinforcement learning as sequence modeling using the Transformer architecture

Dependents

These beliefs depend on this one:

• OUT decision-transformer-dissolves-rl-sequence-modeling-boundary — Decision Transformer's recasting of reinforcement learning as sequence modeling dissolves the boundary between RL and sequence prediction — combining with the broader dissolution of classical paradigm boundaries (supervised/unsupervised/RL), this represents a specific mechanism by which the Transformer architecture actively drives paradigm taxonomy dissolution rather than merely being affected by it.
• IN rl-paradigm-dissolution-validates-crisis-universality-in-temporal-domain — Two independent RL developments — Decision Transformer dissolving the RL/sequence-modeling boundary by absorbing RL into the Transformer's native modality, and DeepSeek-R1 eliminating the supervised fine-tuning step from the LLM pipeline — jointly validate that paradigm taxonomy dissolution extends into the temporal/decision-making domain, not just the perceptual (CV) and linguistic (NLP) domains, establishing that the crisis dynamic is truly universal across all data modalities.