mlsanityData to 14 Oct 2026

TRACE-CASH: Trial-History-Conditioned Reinforcement Learning for Adaptive Configuration Exploration in Time-Series CASH

arXiv 2608.16410 ↗First posted 17 Aug 2026cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL training of language and agent models

Abstract (arXiv)

Combined algorithm selection and hyperparameter optimization (CASH) searches a conditional space in which the selected model determines which hyperparameters are active. In time-series forecasting, temporal choices, chronological validation, and costly evaluations further complicate this search. Controlled comparisons of heterogeneous search methods under a shared time-series CASH (TS-CASH) evaluation protocol remain limited. Within this setting, we study TRACECASH, a task-local hybrid sequential optimizer combining grouped actor-critic candidate generation with fixed rules for model coverage, validation-guided exploitation, and exploration after stalled progress. A model actor proposes an initial forecasting model; three model-conditioned actors generate temporal, architectural, and training actions; and a modelspecific decoder constructs the configuration ultimately evaluated. We compare TRACE-CASH with six alternatives spanning random, Bayesian, evolutionary, multi-objective, and language-model-assisted search across 41 dataset-frequency task variants. TRACE-CASH has the lowest mean rank on both MASE and WQL. Descriptively, it also has the lowest window-averaged test-MASE rank in the predefined full and late windows. These results support the complete TRACECASH procedure as competitive among the evaluated methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL training of language and agent models, one of 15 directions in Deep reinforcement learning methods. Builds on 9 of its 20 references that are in the map. First posted on arXiv 17 Aug 2026.

Builds on

9 of 20 references.

References · 9

  1. 2013Auto-Encoding Variational Bayescited by 4,053
  2. 2021Monash Time Series Forecasting Archivecited by 267
  3. 2024TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methodscited by 249
  4. 2024Large Language Models to Enhance Bayesian Optimizationcited by 135
  5. 2021YAHPO Gym -- An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimizationcited by 62
  6. 2024Sequential Large Language Model-Based Hyper-parameter Optimizationcited by 24
  7. 2022Review of automated time series forecasting pipelinescited by 18
  8. 2021AutoAI-TS: AutoAI for Time Series Forecastingcited by 15
  9. 2026Tree-Structured Synergy of Large Language Models and Bayesian Optimization for Efficient CASHcited by 2

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