arXiv AI
Jun 2
Extreme Low-Bit Inference in Reasoning Models: Failure Modes and Targeted Recovery
arXiv:2606. 02011v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) rely on long reasoning traces, making inference expensive.
Extreme Low-Bit Inference in Reasoning Models: Failure Modes and Targeted Recovery
Large Reasoning Models (LRMs) rely on long reasoning traces, making inference expensive. While low-bit quantization reduces per-token decoding cost, we show that aggressive 2-bit inference can fail to deliver end-to-end speedup because instability in the generation process inflates total token count.
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arXiv Machine Learning
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Train Where the Quantized Model Goes: On-Policy Distillation for Low-Bit Reasoning
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arXiv Machine Learning
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arXiv Machine Learning
Jun 2
Quantized Reasoning Models Think They Need to Think Longer, but They Do Not
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arXiv Machine Learning
Jun 26
What Survives When You Compress a Recursive Reasoner for the Edge?
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arXiv Computation and Language
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TRACES: Tagging Reasoning Steps for Adaptive Cost-Efficient Early-Stopping
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Hugging Face Trending Papers
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Fewer Tokens, Smaller Cache: Reward-Coordinated Efficient Reasoning
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arXiv AI
4d ago
MetaCtrl: Your Large Language Models Can Reason Better and More Concisely with a Metacognitive Controller
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arXiv AI
Jul 21
Can We Break LLMs Out of Self-Loops? Fine-Grained Reasoning Control with Activation Steering
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arXiv AI
Jun 4
Speculative Thinking: Enhancing Small-Model Reasoning with Large Model Guidance at Inference Time
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