Trust Guided Decision Transformer
Decision Transformer performance degrades on long rollouts because the conditioning context drifts out of the training distribution. We show that this drift is visible through the model's own next state prediction error, which rises during rollout and stays elevated, giving a direct signal of when context has become unreliable. We introduce Trust Guided Decision Transformer (TGDT), which selects context before applying value guidance. At each step, TGDT evaluates several recent context suffixes using rolling next state prediction error, calibrated against held out offline data via split conformal prediction. It keeps only suffixes whose error stays within the calibrated threshold, then us...
| Authors | Chainesh Gautam, Raghuram Bharadwaj Diddigi, Chandramouli Kamanchi et al. |
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| Primary category | cs.LG |
| Published | 25 September 2026 |
| Last updated at source | 5 October 2026 |
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