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This paper introduces state commitment learning, a training objective that teaches language models to distinguish temporary computation tokens from persistent state tokens. The authors propose Counterfactual Erasure RL (CERL) and the Erasure Dependence Protocol, showing improvements across math, logic, science QA, and tool-use tasks without sacrificing accuracy.
This article provides a proof that Jira's automation features are Turing-complete by implementing a Minsky register machine using Jira issues and automation rules.