ReinPatch
Summary
ReinPatch is a learned adaptive patching method for time-series forecasting. It trains a standalone patch-boundary policy with reinforcement learning and uses the downstream forecasting loss as the reward signal.
Role In The Wiki
ReinPatch is the current time-series case study for dynamic tokenization inspired by H-Net-like learned chunking. Its value is not that it becomes a full forecasting foundation model, but that it shows how a detachable patcher can be trained, frozen, and transferred across forecasting datasets.
Interface
- Input unit: numeric time-series observations.
- Patch decision: binary or multi-level boundary labels over the input sequence.
- Optimization: Group Relative Policy Gradient over sampled boundary sets.
- Downstream role: compress the temporal context before a forecasting backbone processes it.
- Transfer mode: freeze a pretrained foundation patcher and train only the downstream backbone.
Evidence
Relation To Foundation TSFM Agenda
Use the source-level agenda mapping in reinpatch-2026 rather than duplicating verdict rows here.
At the entity level, ReinPatch is the current time-series case study for dynamic tokenization inspired by H-Net-like learned chunking. Its value is not that it becomes a full forecasting foundation model, but that it shows how a detachable patcher can be trained, frozen, and transferred across forecasting datasets. This page should stay as the object card; source pages carry slot-level verdicts, evidence, and missing pieces.