DPDM Improves Agent Decision Making via Shared Predictor
Original title: Decoupled Predictive Decision Making: A Shared Conditional Predictor for Agents
DPDM raises mean accuracy from 78.80% to 84.92% across 50 benchmarks using one frozen predictor.
Agent decisions depend on empirical relationships revealed by observations. DPDM formulates decisions as conditional prediction with a shared pretrained learner. Semantic representations come from language models while structured pretraining supplies inference capability without parameter updates.
- Mean accuracy improves from 78.80% to 84.92% on 50 benchmarks
- 34.26% absolute gain on ToolPRMBench BFCL
- 28.87% absolute gain on ToolSandbox
- Accuracy improves as history budgets increase
From the original article
1 min read at huggingface.co
Agent decisions depend on empirical relationships that deployment observations reveal and task descriptions alone may leave unresolved. We introduce Decoupled Predictive Decision Making (DPDM), an architecture that formulates heterogeneous agent decisions as conditional prediction with a shared pretrained learner.
By Lang Mo, Xingxuan Zhang, Qi Wang · huggingface.co
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