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Performance-Driven Environment Abstraction with Multi-Timescale Learning

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arXiv:2606. 17377v1 Announce Type: new Abstract: We study performance-driven environment abstraction for decision-making in large Markov decision processes.

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Authors: Yue Guan, Dipankar Maity, Panagiotis Tsiotras
Released: 2026-06-17 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because Performance-Driven Environment Abstraction with Multi-Timescale Learning overlaps with clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "Training one persona to emit a [ZLT] marker without bystanders adopting it has a one-cell-wide LR x epochs window on Qwen2.5-7B-Instruct (LOW confidence)", clean result "A pretraining-data-poisoned Qwen3-4B backdoor only fires on the exact trigger tokens — paraphrases don't activate it, and base-model similarity to the trigger doesn't predict which inputs fire (MODERATE confidence)". Matching terms: rect, line, rate, compare, control, model. Source: arxiv cs.LG (Machine Learning).
arXiv: 2606.17377

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