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Georgia Institute of Technology
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Expert traces can enhance multi-turn tool-use agents without constraining their rollout generation, leading to significant performance gains.
Ditch the slow lane: $R^2$-dLLM turbocharges diffusion language models by slashing decoding steps by up to 75% without sacrificing quality.
Discrete diffusion language models can now achieve higher accuracy without retraining the entire backbone, thanks to a lightweight recurrent memory module that bridges denoising steps.
