LoopAhead: Speculating Ahead for Faster Decoding in Looped Language Models

Key points

• Looped Transformers improve parameter efficiency by repeatedly applying the same Transformer layers.1
• Recent pipelined self-speculative decoding methods reduce this delay by drafting tokens from early-loop predictions and asynchronously advancing tokens at different recurrent depths in the same batch, but they do not fully exploit early-loop features for longer lookahead and still spend computation verifying incorrect speculative branches.1
• A lightweight lookahead drafter (LAD) predicts multiple future tokens with early-loop states, and deeper-loop feedback helps decide which draft branches to continue and which to pause, while the full-depth model makes the final token decisions.1
• LoopAhead achieves up to 7.82x decoding speedup over autoregressive decoding while preserving the target model's output distribution.1

Sources (1)

• [1]LoopAhead: Speculating Ahead for Faster Decoding in Looped Language Models
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 09:38 AM

  We introduce \method, a speculative decoding framework for looped transformers that enables earlier drafting of future tokens and assigns complementary roles to different recurrent depths.

  Looped Transformers improve parameter efficiency by repeatedly applying the same Transformer layers.

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