arXiv:2604. 27201v3 Announce Type: replace-cross Abstract: Hybrid-thinking language models expose explicit /think and /no_think modes, but current designs do not separate them cleanly.
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arXiv:2606. 01080v1 Announce Type: cross Abstract: Large language models often improve on difficult tasks by spending inference-time compute on a reasoning trace before producing the final answer.
arXiv:2609.16055v1 Announce Type: cross Abstract: Test-time compute has emerged as a major approach to improving the capabilities of Large Language Models (LLMs). However, existing test-time reasonin...
arXiv:2504. 12329v2 Announce Type: replace-cross Abstract: Recent advances leverage post-training to enhance model reasoning performance, which typically requires costly training pipelines and still suffers from inefficient, overly lengthy outputs.
LEV is a decision model that performs reasoning by looping its layers instead of generating text, allowing it to deepen its computation on a per-question basis. Trained jointly across all loop steps, a single LEV checkpoint can answer at any depth, improving accuracy—especially on hard questions—while keeping inference fast. On benchmark tests, LEV outperforms dense and cascade models by 5.4 to 8.8 accuracy points with sub‑50 ms latency, and on‑demand depth selection saves 15–24% of computation for minimal accuracy loss.
The paper introduces Thinking-Free Policy Initialization (TFPI), a simple adaptation to Reinforcement Learning with Verifiable Reward (RLVR) that removes explicit thinking content during training and inference. TFPI bridges long Chain-of-Thought distillation with standard RLVR, reducing token usage and improving performance. Experiments show that TFPI accelerates RL convergence, raises performance ceilings, and yields token‑efficient reasoning models, enabling a 4B model to achieve 89.0% accuracy on AIME24 and 65.5% on LiveCodeBench with under 4K H20 hours.
ThinkBit is a two‑bit scalar quantization framework designed to preserve reasoning capabilities in language models. It assigns each layer a ternary, 2‑bit, or 3‑bit tier based on sensitivity, then uses a Gumbel‑softmax search to find optimal codewords block‑by‑block. After freezing codewords, it recalibrates shared scales via knowledge‑distillation and performs a brief quantization‑aware training, finally selecting the best checkpoint with paired reasoning tests.