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Inner Thinking Transformer: Leveraging Dynamic Depth Scaling to Foster Adaptive Internal Thinking

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arxiv 2502.13842 v2 pith:BQIEWTFY submitted 2025-02-19 cs.CL

Yilong Chen , Junyuan Shang , Zhenyu Zhang , Yanxi Xie , Jiawei Sheng , Tingwen Liu , Shuohuan Wang , Yu Sun
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Hua Wu Haifeng Wang
This is my paper
classification cs.CL
keywords thinkingtransformerparameterperformancetokensacrossadaptivecomputation
verification ladder T0 review T1 audit T2 compute T3 formal
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Large language models (LLMs) face inherent performance bottlenecks under parameter constraints, particularly in processing critical tokens that demand complex reasoning. Empirical analysis reveals challenging tokens induce abrupt gradient spikes across layers, exposing architectural stress points in standard Transformers. Building on this insight, we propose Inner Thinking Transformer (ITT), which reimagines layer computations as implicit thinking steps. ITT dynamically allocates computation through Adaptive Token Routing, iteratively refines representations via Residual Thinking Connections, and distinguishes reasoning phases using Thinking Step Encoding. ITT enables deeper processing of critical tokens without parameter expansion. Evaluations across 162M-466M parameter models show ITT achieves 96.5\% performance of a 466M Transformer using only 162M parameters, reduces training data by 43.2\%, and outperforms Transformer/Loop variants in 11 benchmarks. By enabling elastic computation allocation during inference, ITT balances performance and efficiency through architecture-aware optimization of implicit thinking pathways.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Scaling Latent Reasoning via Looped Language Models

    cs.CL 2025-10 unverdicted novelty 7.0 of 10

    Looped language models with latent iterative computation and entropy-regularized depth allocation achieve performance matching up to 12B standard LLMs through superior knowledge manipulation.

  2. HALO: Hybrid Adaptive Latent Reasoning for Language Models

    cs.CL 2026-05 conditional novelty 5.0 of 10

    Selective second-stage latent refinement on budgeted scored tokens beats uniform fixed-1 and fixed-2 refinement on frozen Phi-4-mini for average MMLU-Pro/GPQA score at lower average applied refine steps.

  3. Reasoning on a Budget: A Survey of Adaptive and Controllable Test-Time Compute in LLMs

    cs.AI 2025-07 conditional novelty 5.0 of 10

    The survey's L1/L2 taxonomy and benchmark show that current reasoning models waste compute on easy problems and underthink hard ones, motivating more adaptive inference.

  4. Skip a Layer or Loop it? Test-Time Depth Adaptation of Pretrained LLMs

    cs.LG 2025-07 reject novelty 4.0 of 10

    Pretrained LLM layers can be skipped/repeated per input to build custom paths, but the search uses ground-truth answers, so the accuracy gains are fitted, not predicted.

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