Metacognitive controller trims diffusion language-agent inference cost
Original title: Metacognitive Decoding: Efficient Inference for Diffusion Language Agents
Across five benchmarks, the plug-in cuts inference FLOPs 20.6% on average versus the strongest efficient baseline while keeping competitive accuracy.
Diffusion language models can generate tokens in parallel, but agents still need to decide how much text to produce. The authors add a lightweight controller, trained on agent trajectories, that decides when to finalize tokens and how far generation should extend. On five benchmarks they report a 20.6% average reduction in inference FLOPs versus the most efficient baseline, with competitive task accuracy. Ablations are presented as support for the design.
Unconfirmed: The FLOP and accuracy figures are the authors' reported results.
- The controller is a plug-in for a diffusion backbone.
- Training balances response correctness against generation cost.
- It adapts both computation spent and how much text is generated.
- Ablation studies are reported to support the controller.
From the original article
1 min read at arxiv.orgDiffusion language models offer a promising path to efficient agents through parallel token generation. However, efficient interaction requires adapting both where computation is spent and how much text is generated. We introduce a metacognitive controller that guides diffusion generation according to the needs of the current response and the agent's interaction with the environment.
By Mingyu Zhao · arxiv.org
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