Teaching LLMs to think in latent space instead of generating explicit steps.
By adding a brain's working memory to LLMs through dynamic cache augmentation.
A coprocessor augments frozen LLMs with latent embeddings, enabling better reasoning without architectural changes or explicit intermediate steps.
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https://arxiv.org/abs/2412.17747
🤔 Original Problem:
LLMs need extra "thinking steps" for complex reasoning, but current methods generate these steps sequentially as tokens, causing latency and optimization challenges.
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🔧 Solution in this Paper:
→ The method introduces a coprocessor that works alongside a frozen LLM, processing its key-value cache.
→ This coprocessor generates latent embeddings in a single forward pass, not sequentially like traditional methods.
→ The system trains only the coprocessor using standard language modeling loss, keeping the base LLM unchanged.
→ The coprocessor can run asynchronously and offline, making it computationally efficient.
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🎯 Key Insights:
→ Latent embeddings can replace explicit reasoning steps
→ Asynchronous operation reduces computational overhead
→ End-to-end differentiability improves training efficiency
→ Performance scales with number of latent embeddings
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📊 Results:
→ 10.05% accuracy improvement on GSM8K with 64 latent embeddings
→ 4.70% improvement on MMLU benchmark
→ Consistent perplexity reduction across various token positions
→ Benefits extend up to 32 tokens ahead
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