Human-Aligned Decision Transformers for heritage language revitalization programs under real-time policy constraints

Dev.to / 6/16/2026

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Key Points

The article argues that standard next-token language modeling often fails for endangered heritage languages due to limited data and missing cultural pragmatics.
It proposes adapting Decision Transformers to treat language generation as sequential decision-making driven by “returns-to-go,” enabling better planning under constraints.
Policy and cultural requirements (e.g., prioritizing dialects, balancing purity vs. practicality, limiting modern loanwords) can be encoded as reward functions or penalties.
The approach also incorporates human feedback from elders and speakers to align generated conversational content with cultural prestige and context.
The author plans to share the end-to-end journey—failures, breakthroughs, and code—of applying DTs to Māori revitalization under real-time policy constraints.

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