Scaling Up Test-Time Compute with Latent Reasoning with Jonas Geiping - #723

2025-03-17 · 59 min · episode 723 · 13 entities

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→ appeared on The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence podcast

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Scaling Up Test-Time Compute with Latent Reasoning with Jonas Geiping - #723
→ hosted by Sam Charrington person

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Feed author/publisher: Sam Charrington
→ discusses Science concept

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→ discusses Technology company

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Feed category: Tech News
→ hosted by TWIML company

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Feed category: Science
mentioned Tech News concept

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Feed category: Tech News
discusses Science concept

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evidence rules-v4
Feed category: Science
discusses News concept

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evidence rules-v4
Feed category: News
discusses Tech News concept

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evidence rules-v4
Feed category: Tech News

companys 4

mentioned TWIML company

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Feed author/publisher: TWIML
mentioned Technology company

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evidence rules-v4
Feed category: Technology
discusses Technology company

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Feed category: Technology
hosted by TWIML company

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Feed author/publisher: TWIML

persons 3

mentioned Latent Reasoning with Jonas Geiping person

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evidence rules-v4
Scaling Up Test-Time Compute with Latent Reasoning with Jonas Geiping - #723
mentioned Sam Charrington person

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evidence rules-v4
Feed author/publisher: Sam Charrington
hosted by Sam Charrington person

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evidence rules-v4
Feed author/publisher: Sam Charrington

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appeared on The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence podcast

0.68

evidence rules-v4
Scaling Up Test-Time Compute with Latent Reasoning with Jonas Geiping - #723

Episode description as stored

Today, we're joined by Jonas Geiping, research group leader at Ellis Institute and the Max Planck Institute for Intelligent Systems to discuss his recent paper, “Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach.” This paper proposes a novel language model architecture which uses recurrent depth to enable “thinking in latent space.” We dig into “internal reasoning” versus “verbalized reasoning”—analogous to non-verbalized and verbalized thinking in humans, and discuss how the model searches in latent space to predict the next token and dynamically allocates more compute based on token difficulty. We also explore how the recurrent depth architecture simplifies LLMs, the parallels to diffusion models, the model's performance on reasoning tasks, the challenges of comparing models with varying compute budgets, and architectural advantages such as zero-shot adaptive exits and natural speculative decoding. The complete show notes for this episode can be found at https://twimlai.com/go/723.