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News/Experience-Driven Dynamic Exits for LLMs with Reinforcement Learning
arxiv
PublishedJune 3, 2026 at 4:00 AM
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Experience-Driven Dynamic Exits for LLMs with Reinforcement Learning

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Publisher summary· verbatim

arXiv:2606.03113v1 Announce Type: new Abstract: Large Language Models suffer from slow autoregressive inference. While self-speculative decoding accelerates this process, its efficiency is hampered by static configurations like fixed exit layers and speculation lengths. We reframe this optimization

Models mentioned
02
  • 01meta-llama logo
    Llama-2
    meta-llama/Llama-2
  • 02meta-llama logo
    Llama-3
    meta-llama/Llama-3
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  • arxivJun 17
    Combating Data Laundering in LLM Training
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Mentioned models
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    Llama-2
    meta-llama/Llama-2
  • 02
    Llama-3
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#optimization#reinforcement-learning#language-models
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Meta

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Mentioned models
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  • 01
    Llama-2
    meta-llama/Llama-2
  • 02
    Llama-3
    meta-llama/Llama-3
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arxiv
Read original ↗All from arxiv →
Tags
03
#optimization#reinforcement-learning#language-models
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Meta

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