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arxiv: 2510.12773 · v2 · pith:LYQICRXNnew · submitted 2025-10-14 · 💻 cs.CL · cs.AI· cs.LG

Dr.LLM: Dynamic Layer Routing in LLMs

Ahmed Heakl , Martin Gubri , Salman Khan , Sangdoo Yun , Seong Joon Oh This is my paper

Pith reviewed 2026-05-21 19:56 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.LG
keywords dynamic layer routingLLM efficiencyadaptive computationMCTS supervisionfrozen model retrofittingper-layer routersbudget-aware inference
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The pith

Dr. LLM retrofits frozen LLMs with lightweight routers that learn to skip, execute, or repeat layers using MCTS supervision.

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

The paper presents Dr. LLM as a way to equip pretrained language models with per-layer routers that decide whether to skip, run, or repeat each transformer block. These routers are trained explicitly on high-quality layer sequences discovered by Monte Carlo Tree Search, so the model can spend fewer layers on easy inputs and more on hard ones. The approach keeps the original model weights unchanged and adds only small bottleneck MLPs plus windowed pooling and focal loss to handle imbalance. A sympathetic reader would care because current LLMs waste computation by always using every layer, while this method aims to deliver accuracy gains and compute savings at inference time without full retraining.

Core claim

Dr. LLM equips pretrained models with lightweight per-layer routers deciding to skip, execute, or repeat a block; routers are trained with explicit supervision from MCTS-derived high-quality layer configurations that preserve or improve accuracy under a compute budget.

What carries the argument

Lightweight per-layer bottleneck MLP routers, trained with windowed pooling and focal loss on MCTS supervision, that output skip/execute/repeat decisions for each transformer block.

Load-bearing premise

High-quality layer configurations found by MCTS on in-domain tasks provide supervision signals that lightweight routers can reliably learn and generalize to out-of-domain tasks with only minimal accuracy loss.

What would settle it

Routers trained on ARC and DART layer paths produce more than a 0.85 percent accuracy drop or fail to reduce average layer count when tested on MMLU, GSM8k, or GPQA.

Figures

Figures reproduced from arXiv: 2510.12773 by Ahmed Heakl, Martin Gubri, Salman Khan, Sangdoo Yun, Seong Joon Oh.

Figure 1
Figure 1. Figure 1: Dr.LLM improves ac￾curacy while reducing computa￾tion. Number of layers used per example vs. accuracy on ARC and DART, averaged on six models. Large language models (LLMs) typically process every token through a fixed stack of transformer layers, regardless of the input’s difficulty. This static-depth regime results in wasted computation for easy prompts and insufficient flexibility for challenging reasoni… view at source ↗
Figure 2
Figure 2. Figure 2: Our layer routing based on hidden states. Dr.LLM augments a frozen decoder-only LLM with per-layer routers that decide to skip, execute, or repeat a block once. Routers read windowed summaries of hidden states and are trained from MCTS￾derived targets (Sec. 4). For clarity, the diagram also highlights the router internals and the flow of hidden states across layers. Skip layers Execute Layer-wise Routing R… view at source ↗
Figure 3
Figure 3. Figure 3: Length-aware MCTS used to collect the supervised training dataset of per-layer routing configurations (skip/execute/repeat). For each input, MCTS explores modified layer paths and retains accuracy-preserving or improving ones under a compute budget. To stabilize decisions on long contexts while keeping overhead negligible, we adopt windowed mean pooling: the first W⌊T /W⌋ tokens are divided into contiguous… view at source ↗
Figure 4
Figure 4. Figure 4: Analysis of routing decisions per layer, dataset, and model. (a) Layer frequency of LLaMa 3B and 8B base (B) and instruct (I) models across ARC and DART. (b,c) Layer frequency grouped by early, middle, and late layers. The x-axis corresponds to the dataset difficulty levels: ARC-Easy (A-1), ARC-Challenge (A-2), and DART levels 1–5 (from D-1 to D-5). computation. Thus, Dr.LLM not only yields efficiency and … view at source ↗
Figure 5
Figure 5. Figure 5: Ablation study. We apply Dr.LLM on LLaMa3.2-3B and control: (a) the effect of bottle￾neck dimension, (b) the effect of number of linear layers, and (c) the effect of number of windows. 6.5 ABLATION STUDIES Router internals. We ablate the router components to understand their effect on accuracy and efficiency ( [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Fine-grained control in LLaMA-8B. (a) Accuracy as a function of interpolated routing decisions, compared to baseline (red) and ours (green). (b) Histogram of routing probabilities. Shifts from execute → skip correlate with higher accuracy, while repeat allocations increase computation. 13 [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Effect of loss choice under class imbalance. Macro F1 across training for weighted CE, focal, and plain CE. While all losses perform similarly on the majority execute class, only focal loss improves skip accuracy and yields non-trivial repeat accuracy, highlighting its necessity for minority classes. C FINE-GRAINED CONTROL OF ROUTER DECISIONS Beyond analyzing learned routing policies, we study whether rout… view at source ↗
Figure 8
Figure 8. Figure 8: Effect of window size on router training. Larger pooling windows consistently improve minority-class accurac. Gains saturate beyond 16 windows, suggesting diminishing returns. E TRAINING ON MORE WINDOWS Windowed mean pooling stabilizes router decisions by aggregating hidden states over larger con￾texts [PITH_FULL_IMAGE:figures/full_fig_p015_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Label distribution across models. Distribution of skip/execute/repeat actions across datasets for different planners: (a) LLaMA-3B, (b) LLaMA-8B, (c) LLaMA-Base-3B, (d) LLaMA￾Base-8B, (e) Qwen-3B, (f) Qwen-7B. that deeper refinement is allocated where multi-step reasoning is required. Instruction-tuned mod￾els exhibit more aggressive skipping than base models, supporting the view that fine-tuning creates f… view at source ↗
Figure 10
Figure 10. Figure 10: Per-layer routing frequency across datasets and models. Heatmaps show the mean usage per layer (0 = skip, 1 = execute, 2 = repeat) for six backbones: (a) LLaMA-3.2-3B-Instruct, (b) LLaMA-3.1-8B-Instruct, (c) LLaMA-3.2-3B-Base, (d) LLaMA-3.1-8B-Base, (e) Qwen2.5-3B￾Instruct, and (f) Qwen2.5-7B-Instruct. The x-axis corresponds to benchmark subsets (ARC-E, ARC￾C, DART1–5). Early layers are consistently execu… view at source ↗
read the original abstract

Large Language Models (LLMs) process every token through all layers of a transformer stack, causing wasted computation on simple queries and insufficient flexibility for harder ones that need deeper reasoning. Adaptive-depth methods can improve efficiency, but prior approaches rely on costly inference-time search, architectural changes, or large-scale retraining, and in practice often degrade accuracy despite efficiency gains. We introduce Dr. LLM, Dynamic routing of Layers for LLMs, a retrofittable framework that equips pretrained models with lightweight per-layer routers deciding to skip, execute, or repeat a block. Routers are trained with explicit supervision: using Monte Carlo Tree Search (MCTS), we derive high-quality layer configurations that preserve or improve accuracy under a compute budget. Our design, windowed pooling for stable routing, focal loss with class balancing, and bottleneck MLP routers, ensures robustness under class imbalance and long sequences. On ARC (logic) and DART (math), Dr. LLM improves accuracy by up to +3.4%p while saving 5 layers per example on average. Routers generalize to out-of-domain tasks (MMLU, GSM8k, AIME, TruthfulQA, SQuADv2, GPQA, PIQA, AGIEval) with only 0.85% accuracy drop while retaining efficiency, and outperform prior routing methods by up to +7.7%p. Overall, Dr. LLM shows that explicitly supervised routers retrofit frozen LLMs for budget-aware, accuracy-driven inference without altering base weights. Code is available at https://github.com/parameterlab/dr-llm.

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 / 2 minor

Summary. The manuscript presents Dr. LLM, a retrofittable framework for dynamic layer routing in pretrained LLMs. Lightweight per-layer bottleneck-MLP routers, trained via supervised imitation of MCTS-derived skip/execute/repeat configurations on in-domain tasks (ARC, DART), decide layer usage at inference time. The design incorporates windowed pooling and focal loss with class balancing. Reported results include up to +3.4%p accuracy gains and 5-layer average savings on in-domain tasks, +7.7%p outperformance versus prior routers, and generalization to OOD benchmarks (MMLU, GSM8k, etc.) with only 0.85% accuracy drop, all without modifying base model weights.

Significance. If the results hold, the work demonstrates a practical, low-cost way to equip frozen LLMs with budget-aware adaptive depth via explicit external supervision, avoiding full retraining or expensive inference-time search. The combination of MCTS-derived targets, focal loss, and windowed pooling addresses class imbalance and sequence-length issues in a concrete, reproducible manner; the public code release further strengthens the contribution for deployment-oriented efficiency research.

major comments (2)
  1. [OOD evaluation] OOD evaluation section: The central claim that MCTS-derived configurations on ARC/DART supply supervision signals that routers can reliably approximate on unseen tasks rests on the 0.85% OOD accuracy drop. However, the manuscript provides no router-decision histograms, agreement rates with MCTS optima, or per-task ablation on OOD inputs; without these, it remains possible that routers default to full execution under distribution shift and that the small drop is not attributable to successful transfer of the learned policy.
  2. [Results] Experimental details (Tables reporting accuracy and layer savings): The abstract and results cite concrete gains (+3.4%p, 5-layer savings, +7.7%p vs. priors) but the provided text does not include variance across seeds, full baseline implementations, or statistical significance tests. These omissions make it difficult to assess whether the efficiency-accuracy trade-off is robust or sensitive to hyper-parameter choices such as the focal-loss balancing weights.
minor comments (2)
  1. [Method] Notation for router output (skip/execute/repeat) should be defined explicitly with a small equation or table early in the method section to avoid ambiguity when discussing the three-way classification.
  2. [Method] The manuscript mentions 'windowed pooling for stable routing' but does not specify the window size or pooling operation in sufficient detail for reproduction; a short pseudocode block or hyper-parameter table would help.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below, providing clarifications and committing to revisions that strengthen the evidence for our claims without altering the core contributions.

read point-by-point responses

  1. Referee: [OOD evaluation] OOD evaluation section: The central claim that MCTS-derived configurations on ARC/DART supply supervision signals that routers can reliably approximate on unseen tasks rests on the 0.85% OOD accuracy drop. However, the manuscript provides no router-decision histograms, agreement rates with MCTS optima, or per-task ablation on OOD inputs; without these, it remains possible that routers default to full execution under distribution shift and that the small drop is not attributable to successful transfer of the learned policy.

    Authors: We agree that additional diagnostics would make the transfer claim more robust. In the revised manuscript we will add router-decision histograms and per-layer agreement rates with the MCTS-derived targets for representative OOD tasks (MMLU, GSM8k, GPQA). We will also include a per-task ablation table that reports average layer usage and accuracy when the router is forced to full execution versus its learned policy. These figures will show that the routers continue to produce non-trivial skip/repeat decisions on OOD inputs and that the observed layer savings are not an artifact of defaulting to the full model. revision: yes

  2. Referee: [Results] Experimental details (Tables reporting accuracy and layer savings): The abstract and results cite concrete gains (+3.4%p, 5-layer savings, +7.7%p vs. priors) but the provided text does not include variance across seeds, full baseline implementations, or statistical significance tests. These omissions make it difficult to assess whether the efficiency-accuracy trade-off is robust or sensitive to hyper-parameter choices such as the focal-loss balancing weights.

    Authors: We acknowledge the value of reporting variability and formal significance testing. We have re-executed the main experiments across five random seeds and will report means and standard deviations in all accuracy and layer-savings tables. Full re-implementation details for the prior routers (including hyper-parameter settings used for fair comparison) will be added to the appendix. We will also include a sensitivity analysis on the focal-loss gamma and class-balancing weights, together with paired statistical tests (Wilcoxon signed-rank) against the strongest baseline to quantify the reliability of the reported gains. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation relies on external MCTS supervision and held-out evaluation

full rationale

The paper derives router decisions via explicit supervision from Monte Carlo Tree Search (MCTS) run on the frozen base LLM to produce high-quality skip/execute/repeat configurations on in-domain tasks (ARC, DART). These configurations serve as training targets for lightweight bottleneck-MLP routers using focal loss and windowed pooling; the routers are then evaluated for accuracy and efficiency on separate out-of-domain benchmarks (MMLU, GSM8k, etc.). This is ordinary imitation learning followed by external validation rather than any self-referential fit, parameter renaming, or self-citation chain that reduces the central claim to its own inputs. No equations or steps in the provided description exhibit a prediction that equals its supervision by construction, and the reported 0.85% OOD drop is measured against independent test sets.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The central claim rests on the premise that MCTS can produce reliable supervision targets and that simple MLP routers with focal loss and windowed pooling can learn to replicate them. No new physical entities are introduced.

free parameters (1)
  • focal loss class-balancing weights
    Used to counter severe class imbalance between skip/execute/repeat decisions; concrete values not stated in abstract.
axioms (2)
  • domain assumption Monte Carlo Tree Search can derive high-quality layer configurations that preserve or improve accuracy under a compute budget.
    Invoked to generate the supervision targets for router training.
  • domain assumption Lightweight per-layer routers can make stable decisions from local hidden states without future token information.
    Required for the per-layer routing setup on long sequences.

pith-pipeline@v0.9.0 · 5832 in / 1467 out tokens · 221772 ms · 2026-05-21T19:56:25.492784+00:00 · methodology

discussion (0)

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Reference graph

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    As shown in Fig

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