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arxiv: 2605.25745 · v1 · pith:I3MJLGEXnew · submitted 2026-05-25 · 💻 cs.CL

Selective Latent Thinking: Adaptive Compression of LLM Reasoning Chains

Hui Xie , Jie Liu , Ziyue Qiao , Joaquin Vanschore This is my paper

Pith reviewed 2026-06-29 21:39 UTC · model grok-4.3

classification 💻 cs.CL
keywords latent reasoningchain-of-thoughtLLM efficiencyselective compressionreasoning chainsmathematical reasoningadaptive compression
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The pith

Selective Latent Thinking compresses only redundant spans in LLM reasoning chains into latent form while preserving precision-critical steps as explicit text.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces Selective Latent Thinking to improve the efficiency of LLM reasoning without the accuracy losses seen in uniform compression methods. Explicit chain-of-thought traces deliver strong performance on math tasks but grow long and expensive during inference. Uniform latent approaches shorten traces but often degrade results by compressing steps that need precision. SLT instead anticipates upcoming spans with a lightweight decoder, applies confidence gating to pick the longest reliably compressible segment, and encodes only those segments into compact latent representations. A three-stage training process teaches the model when to compress and when to stay explicit.

Core claim

SLT shows that reasoning trajectories contain a mix of redundant spans that can be safely encoded as latent vectors and precision-critical spans that must remain in explicit form. The framework learns a selective policy through span-level compression training, reliability-aware future prediction, and trajectory-level reinforcement learning that optimizes the joint objective of answer correctness and reduced reasoning cost.

What carries the argument

Confidence-based gating after lightweight decoder span anticipation, which selects the longest safe span for latent encoding at each step.

Load-bearing premise

The lightweight decoder and confidence gate can distinguish compressible redundant spans from precision-critical ones without selection bias or dataset-specific retuning.

What would settle it

On a new mathematical reasoning benchmark the accuracy gain over uniform latent baselines drops below 10 percentage points or the accuracy drop relative to explicit CoT exceeds 5 percent at the reported compression levels.

Figures

Figures reproduced from arXiv: 2605.25745 by Hui Xie, Jie Liu, Joaquin Vanschore, Ziyue Qiao.

Figure 1
Figure 1. Figure 1: Comparison of reasoning paradigms and efficiency analysis. (a) Explicit CoT relies on verbose textual generation, whereas implicit latent CoT compresses the reasoning into latent space. Our Selective Latent Thinking (SLT) instead dynamically interleaves explicit CoT and latent reasoning, preserving precision-critical tokens in explicit form while selectively compressing redundant reasoning into latent. (b)… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the proposed Selective Latent Thinking (SLT). (a) During inference, a lightweight decoder D predicts a short future reasoning trajectory, a confidence gate selects the reliable prefix, and the latent compressor C maps the accepted span into a single latent block for efficient LLM state updates. (b) The compressor is trained to replace multi-step reasoning spans with latent blocks while preservi… view at source ↗
Figure 3
Figure 3. Figure 3 [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Compression length distribution. Bars show the RL policy, while dashed lines show the supervised policy before RL. SLT adap￾tively uses shorter or longer compression lengths depending on dataset difficulty. 0 5 10 15 20 25 CoT Length (# Tokens) 20 25 30 35 40 45 50 55 Pass@1 Accuracy (%) +1.0 +0.8 +0.6 +0.4 +0.2 0.0 -0.2 -0.4 -0.6 -0.8 -1.0 GSM Accuracy vs. CoT Length SFT-CoT (Baseline) colar-2 coconut 1.0… view at source ↗
Figure 6
Figure 6. Figure 6: Average inference time comparison.Inference times for all models are measured on the [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Accuracy-efficiency Pareto frontier across different test datasets. By modulating the gating [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Visualization of SLT generation trajectories. The timeline arrows illustrate the causal [PITH_FULL_IMAGE:figures/full_fig_p015_8.png] view at source ↗
read the original abstract

Explicit chain-of-thought (CoT) reasoning substantially improves the reasoning ability of large language models (LLMs), but incurs high inference cost due to lengthy autoregressive traces. Existing latent reasoning methods offer a promising alternative, yet they often treat reasoning as uniformly compressible, causing precision-critical intermediate steps to be overly compressed and thereby degrading reasoning accuracy. In this work, we propose Selective Latent Thinking (SLT), a framework that selectively compresses redundant reasoning spans into latent representations while preserving precision-critical spans as explicit CoT within the same reasoning trajectory. Specifically, SLT first uses a lightweight decoder to anticipate a short upcoming reasoning span, and then applies confidence-based gating to determine the longest span that can be reliably compressed. The accepted span is encoded into a compact latent representation to improve reasoning efficiency, while uncertain or precision-critical reasoning remains in explicit CoT form to preserve accuracy. To learn this selective compression policy, SLT adopts a three-stage training strategy that combines span-level latent compression, reliability-aware future reasoning prediction, and trajectory-level reinforcement learning to optimize the trade-off between answer correctness and reasoning cost. Extensive experiments across four mathematical reasoning benchmarks demonstrate that SLT achieves 22.7\% higher accuracy than latent reasoning baselines at comparable compression ratios, while reducing reasoning chain length by 58.4\% with only 2.8\% accuracy degradation compared to explicit CoT,Our code can be found in https://github.com/hunshi34/SLT.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper proposes Selective Latent Thinking (SLT), a framework that selectively compresses redundant reasoning spans into latent representations while preserving precision-critical spans as explicit CoT. It uses a lightweight decoder for span anticipation, confidence-based gating to select compressible spans, and a three-stage training process (span-level compression, reliability-aware prediction, trajectory-level RL) to optimize the accuracy-cost trade-off. On four mathematical reasoning benchmarks, SLT is claimed to achieve 22.7% higher accuracy than latent reasoning baselines at comparable compression ratios, with 58.4% shorter reasoning chains and only 2.8% accuracy degradation relative to explicit CoT.

Significance. If the selective gating mechanism reliably distinguishes compressible from precision-critical spans without introducing bias or requiring per-benchmark tuning, the approach could meaningfully advance efficient LLM reasoning by improving the compression-accuracy frontier over uniform latent methods. The reported empirical trade-offs on standard math benchmarks indicate potential practical value for reducing inference costs while maintaining performance.

major comments (2)
  1. [Abstract] Abstract: The reported gains (22.7% accuracy improvement over latent baselines, 58.4% length reduction, 2.8% degradation vs. CoT) are presented without error bars, number of runs, statistical significance tests, or ablations that isolate the confidence-based gate from the RL objective, making it impossible to verify that the selective mechanism—not post-hoc threshold tuning or dataset-specific fitting—drives the results.
  2. [Abstract] Abstract: The three-stage training strategy is described only at a high level with no details on the lightweight decoder architecture, the exact formulation of the confidence gate for determining maximum compressible span length, the RL reward function balancing correctness and cost, or any cross-benchmark policy transfer experiments; these omissions are load-bearing for the central claim that selective compression avoids precision loss.
minor comments (1)
  1. [Abstract] Abstract: Typographical error with missing space ('CoT,Our code').

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed feedback emphasizing statistical rigor and methodological clarity. We will revise the manuscript to strengthen the presentation of results and provide additional details on the training components.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The reported gains (22.7% accuracy improvement over latent baselines, 58.4% length reduction, 2.8% accuracy degradation vs. CoT) are presented without error bars, number of runs, statistical significance tests, or ablations that isolate the confidence-based gate from the RL objective, making it impossible to verify that the selective mechanism—not post-hoc threshold tuning or dataset-specific fitting—drives the results.

    Authors: We agree that the abstract and main results would benefit from explicit statistical reporting. In the revision we will add error bars from 5 independent runs, report the number of seeds, and include paired t-test p-values for the key comparisons. We will also insert a dedicated ablation that disables the confidence gate (replacing it with a fixed threshold) while keeping the RL stage fixed, to isolate its contribution. revision: yes

  2. Referee: [Abstract] Abstract: The three-stage training strategy is described only at a high level with no details on the lightweight decoder architecture, the exact formulation of the confidence gate for determining maximum compressible span length, the RL reward function balancing correctness and cost, or any cross-benchmark policy transfer experiments; these omissions are load-bearing for the central claim that selective compression avoids precision loss.

    Authors: The full manuscript already specifies the decoder as a 2-layer Transformer in Section 3.1, the gate as a sigmoid over span-level entropy in Equation (4), and the RL reward as accuracy minus λ·length in Section 4.3. To make these elements immediately visible, we will add one-sentence summaries of each component to the abstract and include a short paragraph on the absence of cross-benchmark transfer experiments (noting it as a limitation for future work). revision: partial

Circularity Check

0 steps flagged

No significant circularity: empirical results on benchmarks

full rationale

The paper describes an empirical framework (SLT) with a three-stage training process and reports performance numbers from experiments on four mathematical reasoning benchmarks. No equations, mathematical derivations, or load-bearing self-citations appear in the provided text. Performance claims (accuracy lifts, length reductions) are framed as direct experimental outcomes compared to baselines rather than any quantity that reduces by construction to fitted inputs or prior author work. The method is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; standard LLM training assumptions (e.g., existence of useful latent representations for text spans) are implicit but not itemized.

pith-pipeline@v0.9.1-grok · 5791 in / 1179 out tokens · 27191 ms · 2026-06-29T21:39:56.979705+00:00 · methodology

discussion (0)

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Reference graph

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