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arxiv: 2606.04378 · v1 · pith:2CHTSV5Gnew · submitted 2026-06-03 · 💻 cs.CL

DLLG: Dynamic Logit-Level Gating of LLM Experts

Bingnan Li , Zhaoyang Zhang , Xiaoze Liu , Yantao Shen , Shuli Jiang , Shuo Yang , Wei Xia , Zhuowen Tu
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Stefano Soatto
This is my paper

Pith reviewed 2026-06-28 06:48 UTC · model grok-4.3

classification 💻 cs.CL
keywords LLM ensemblinglogit-level gatingdynamic fusionexpert integrationreasoning benchmarkscode generationsparse supervisiontoken-level weighting
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The pith

DLLG learns per-token fusion weights for LLM experts from response-level feedback alone

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

The paper proposes DLLG to combine multiple specialized LLMs without the drawbacks of early routing, fragile heuristics, or parameter interference. A lightweight gating module predicts fusion weights at each generation step, training only on whether the full response is correct. This produces better results than routing, ensembling, or merging baselines on reasoning and code tasks at multiple scales. A sympathetic reader would care because it turns sparse outcome signals into dynamic, token-wise integration of expert strengths during inference.

Core claim

DLLG is a dynamic logit-level ensembling framework that learns token-level expert fusion from sparse response-level supervision. A lightweight gating module predicts step-wise fusion weights, linking trajectory-level correctness to generation without token-level labels or expert retraining. Across diverse reasoning and code benchmarks, DLLG consistently outperforms strong routing, heuristic ensembling, and parameter-merging baselines across model scales.

What carries the argument

The lightweight gating module that predicts step-wise fusion weights to combine expert logits dynamically at each generation step

If this is right

  • DLLG outperforms routing, heuristic ensembling, and parameter-merging baselines on reasoning and code benchmarks
  • The approach scales across different model sizes without retraining the underlying experts
  • Learned logit-level fusion works from sparse response-level signals alone

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same gating idea could extend to mixing experts from different architectures or training regimes
  • Response-level supervision might suffice for learning finer-grained control in other sequential decision tasks
  • Combining this with routing could create hybrid systems that decide both which experts to activate and how to weight their outputs

Load-bearing premise

A lightweight gating module can learn effective step-wise fusion weights that link trajectory-level correctness to per-token generation without requiring token-level labels or expert retraining

What would settle it

Run DLLG on a new reasoning benchmark where the trained gating module produces no accuracy gain over the strongest single expert or any baseline; absence of outperformance would falsify the central claim

Figures

Figures reproduced from arXiv: 2606.04378 by Bingnan Li, Shuli Jiang, Shuo Yang, Stefano Soatto, Wei Xia, Xiaoze Liu, Yantao Shen, Zhaoyang Zhang, Zhuowen Tu.

Figure 1
Figure 1. Figure 1: Comparison of expert combination strategies. Routing relies on hard, early expert selection and fails when subtasks vary within a response. Heuristic ensembling uses inference-time proxy signals that are often misaligned with task correctness. Parameter merging statically fuses expert weights, sacrificing modularity and inducing interference. In contrast, DLLG (ours) performs dynamic, token-level logit fus… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of DLLG, illustrating training, inference, and the gating model architecture in a unified pipeline. (a) Training: Frozen specialized LLMs are conditioned on ground-truth prefixes under teacher forcing to produce hidden states, which are fed into a lightweight gating model. Response-level correctness signals, obtained from automatic verifiers applied to expert rollouts, supervise the gating model v… view at source ↗
Figure 3
Figure 3. Figure 3: Token-level fusion behavior on a representative Code-R1 example, where the gating model dynamically adjusts expert fusion weights, with math-specialized experts dominating early reasoning stages and code-specialized experts becoming more prominent during code generation. token-wise weights from expert trajectory representations, and to combine expert outputs through soft logit-level fusion at inference tim… view at source ↗
Figure 4
Figure 4. Figure 4: Additional visualizations of token-level weights in DLLG across different examples. For each example, the top panel highlights token-wise expert dominance in the fused output, and the bottom panel shows the normalized fusion weights over decoding steps. Across examples, math-specialized experts tend to dominate during early reasoning-heavy segments, while code-specialized experts become more prominent duri… view at source ↗
read the original abstract

Leveraging multiple specialized LLMs can combine complementary strengths, but existing approaches trade adaptability for stability: routing commits prematurely, heuristic ensembling depends on fragile proxies, and parameter merging introduces interference. We propose DLLG (Dynamic Logit-Level Gating), a dynamic logit-level ensembling framework that learns token-level expert fusion from sparse response-level supervision. A lightweight gating module predicts step-wise fusion weights, linking trajectory-level correctness to generation without token-level labels or expert retraining. Across diverse reasoning and code benchmarks, DLLG consistently outperforms strong routing, heuristic ensembling, and parameter-merging baselines across model scales, highlighting learned logit-level fusion as a robust and scalable paradigm for integrating specialized experts.

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

1 major / 1 minor

Summary. The manuscript introduces DLLG, a dynamic logit-level ensembling framework for combining specialized LLMs. A lightweight gating module is trained using only response-level correctness supervision to predict step-wise (per-token) fusion weights during generation, without requiring token-level labels or expert retraining. The central claim is that this approach consistently outperforms strong baselines in routing, heuristic ensembling, and parameter merging across reasoning and code benchmarks and multiple model scales.

Significance. If the dynamic mechanism is validated, the work could establish a scalable paradigm for expert integration that preserves adaptability while using efficient supervision. The avoidance of premature routing commitments and merging interference would be a meaningful advance, particularly if the gains are shown to stem from token-level adaptation rather than ensembling structure alone.

major comments (1)
  1. [Experimental results and analysis sections] The central claim requires that the gating module produces meaningfully varying step-wise fusion weights that adapt during generation and account for the reported gains. However, the manuscript provides no analysis of weight variance, entropy, or token-wise dynamics, nor ablations comparing the learned dynamic weights against their per-response averages (which would reduce to static ensembling). Without this, outperformance could arise from other aspects of the setup rather than the claimed dynamic logit-level mechanism.
minor comments (1)
  1. [Abstract] The abstract would benefit from including at least one quantitative performance delta or specific benchmark result rather than relying solely on the qualitative statement of consistent outperformance.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on strengthening the evidence for the dynamic aspect of the gating mechanism. We agree this analysis is important to substantiate the central claims and will incorporate the suggested elements in the revision.

read point-by-point responses

  1. Referee: [Experimental results and analysis sections] The central claim requires that the gating module produces meaningfully varying step-wise fusion weights that adapt during generation and account for the reported gains. However, the manuscript provides no analysis of weight variance, entropy, or token-wise dynamics, nor ablations comparing the learned dynamic weights against their per-response averages (which would reduce to static ensembling). Without this, outperformance could arise from other aspects of the setup rather than the claimed dynamic logit-level mechanism.

    Authors: We acknowledge the validity of this observation. The current manuscript emphasizes end-to-end performance gains but does not include direct diagnostics of the gating module's behavior. To address this, the revised version will add: (1) quantitative analysis of weight variance and entropy across tokens and responses, (2) examples of token-wise weight trajectories during generation, and (3) an ablation replacing per-token weights with their per-response averages (reducing to static ensembling) and reporting the resulting performance drop on the benchmarks. These additions will isolate the contribution of token-level dynamics. We view this as a necessary strengthening of the experimental section. revision: yes

Circularity Check

0 steps flagged

No significant circularity; gating module trained independently on response-level signals

full rationale

The abstract and available description present DLLG as training a lightweight gating module on sparse response-level correctness to produce step-wise fusion weights. No equations, self-citations, or derivations are shown that reduce the claimed token-level predictions or performance gains to quantities defined by the inputs themselves. The central mechanism is an empirical training procedure rather than a self-definitional or fitted-input construction. This is the most common honest finding for papers whose claims rest on experimental results rather than closed-form derivations.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only abstract available; no explicit free parameters, axioms, or invented entities can be identified.

pith-pipeline@v0.9.1-grok · 5662 in / 989 out tokens · 21494 ms · 2026-06-28T06:48:15.684564+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

46 extracted references · 31 canonical work pages · 16 internal anchors

  1. [1]

    Training Verifiers to Solve Math Word Problems

    Training verifiers to solve math word problems , author=. arXiv preprint arXiv:2110.14168 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  2. [2]

    Advances in neural information processing systems , volume=

    Solving quantitative reasoning problems with language models , author=. Advances in neural information processing systems , volume=

  3. [3]

    Measuring Mathematical Problem Solving With the MATH Dataset

    Measuring mathematical problem solving with the math dataset , author=. arXiv preprint arXiv:2103.03874 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  4. [4]

    Code-R1: Reproducing R1 for Code with Reliable Rewards , author=

  5. [5]

    Evaluating Large Language Models Trained on Code

    Evaluating large language models trained on code , author=. arXiv preprint arXiv:2107.03374 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  6. [6]

    Program Synthesis with Large Language Models

    Program synthesis with large language models , author=. arXiv preprint arXiv:2108.07732 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  7. [7]

    Findings of the Association for Computational Linguistics: ACL 2023 , pages=

    Challenging big-bench tasks and whether chain-of-thought can solve them , author=. Findings of the Association for Computational Linguistics: ACL 2023 , pages=

    2023

  8. [8]

    BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions

    Bigcodebench: Benchmarking code generation with diverse function calls and complex instructions , author=. arXiv preprint arXiv:2406.15877 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  9. [9]

    Findings of the Association for Computational Linguistics: EMNLP 2024 , pages=

    Breaking the ceiling of the llm community by treating token generation as a classification for ensembling , author=. Findings of the Association for Computational Linguistics: EMNLP 2024 , pages=

    2024

  10. [10]

    arXiv preprint arXiv:2410.03777 , year=

    Determine-then-ensemble: Necessity of top-k union for large language model ensembling , author=. arXiv preprint arXiv:2410.03777 , year=

    work page arXiv

  11. [11]

    arXiv preprint arXiv:2404.11531 , year=

    Pack of llms: Model fusion at test-time via perplexity optimization , author=. arXiv preprint arXiv:2404.11531 , year=

    work page arXiv

  12. [12]

    Advances in Neural Information Processing Systems , volume=

    Ensemble learning for heterogeneous large language models with deep parallel collaboration , author=. Advances in Neural Information Processing Systems , volume=

  13. [13]

    arXiv preprint arXiv:2404.09492 , year=

    Bridging the gap between different vocabularies for llm ensemble , author=. arXiv preprint arXiv:2404.09492 , year=

    work page arXiv

  14. [14]

    arXiv preprint arXiv:2502.21265 , year=

    Token-level Ensembling of Models with Different Vocabularies , author=. arXiv preprint arXiv:2502.21265 , year=

    work page arXiv

  15. [15]

    Advances in Neural Information Processing Systems , volume=

    Routerdc: Query-based router by dual contrastive learning for assembling large language models , author=. Advances in Neural Information Processing Systems , volume=

  16. [16]

    arXiv preprint arXiv:2410.02223 , year=

    EmbedLLM: Learning compact representations of large language models , author=. arXiv preprint arXiv:2410.02223 , year=

    work page arXiv

  17. [17]

    arXiv preprint arXiv:2505.19797 , year=

    The Avengers: A Simple Recipe for Uniting Smaller Language Models to Challenge Proprietary Giants , author=. arXiv preprint arXiv:2505.19797 , year=

    work page arXiv

  18. [18]

    arXiv preprint arXiv:2404.14618 , year=

    Hybrid llm: Cost-efficient and quality-aware query routing , author=. arXiv preprint arXiv:2404.14618 , year=

    work page arXiv

  19. [19]

    arXiv preprint arXiv:2407.10834 , year=

    Metallm: A high-performant and cost-efficient dynamic framework for wrapping llms , author=. arXiv preprint arXiv:2407.10834 , year=

    work page arXiv

  20. [20]

    Proceedings of the 17th ACM International Conference on Web Search and Data Mining , pages=

    Fly-swat or cannon? cost-effective language model choice via meta-modeling , author=. Proceedings of the 17th ACM International Conference on Web Search and Data Mining , pages=

  21. [21]

    arXiv preprint arXiv:2505.19435 , year=

    Route to Reason: Adaptive Routing for LLM and Reasoning Strategy Selection , author=. arXiv preprint arXiv:2505.19435 , year=

    work page arXiv

  22. [22]

    arXiv preprint arXiv:2412.04167 , year=

    Bench-CoE: a Framework for Collaboration of Experts from Benchmark , author=. arXiv preprint arXiv:2412.04167 , year=

    work page arXiv

  23. [23]

    Harnessing Multiple Large Language Models: A Survey on LLM Ensemble

    Harnessing multiple large language models: A survey on llm ensemble , author=. arXiv preprint arXiv:2502.18036 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  24. [24]

    RouteLLM: Learning to Route LLMs with Preference Data

    Routellm: Learning to route llms with preference data , author=. arXiv preprint arXiv:2406.18665 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  25. [25]

    International conference on machine learning , pages=

    Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time , author=. International conference on machine learning , pages=. 2022 , organization=

    2022

  26. [26]

    Editing Models with Task Arithmetic

    Editing models with task arithmetic , author=. arXiv preprint arXiv:2212.04089 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  27. [27]

    Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities

    Model merging in llms, mllms, and beyond: Methods, theories, applications and opportunities , author=. arXiv preprint arXiv:2408.07666 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  28. [28]

    Advances in Neural Information Processing Systems , volume=

    Ties-merging: Resolving interference when merging models , author=. Advances in Neural Information Processing Systems , volume=

  29. [29]

    Forty-first International Conference on Machine Learning , year=

    Language models are super mario: Absorbing abilities from homologous models as a free lunch , author=. Forty-first International Conference on Machine Learning , year=

  30. [30]

    arXiv preprint arXiv:2406.11617 , year=

    Della-merging: Reducing interference in model merging through magnitude-based sampling , author=. arXiv preprint arXiv:2406.11617 , year=

    work page arXiv

  31. [31]

    Proceedings of the 12th annual conference on Computer graphics and interactive techniques , pages=

    Animating rotation with quaternion curves , author=. Proceedings of the 12th annual conference on Computer graphics and interactive techniques , pages=

  32. [32]

    Mathematische Zeitschrift , volume=

    How to conjugate C 1-close group actions , author=. Mathematische Zeitschrift , volume=. 1973 , publisher=

    1973

  33. [33]

    arXiv preprint arXiv:2309.15698 , year=

    Deep model fusion: A survey , author=. arXiv preprint arXiv:2309.15698 , year=

    work page arXiv

  34. [34]

    arXiv preprint arXiv:2412.13328 , year=

    Expansion span: Combining fading memory and retrieval in hybrid state space models , author=. arXiv preprint arXiv:2412.13328 , year=

    work page arXiv

  35. [35]

    doi:10.5281/zenodo.12608602 , url =

    Gao, Leo and Tow, Jonathan and Abbasi, Baber and Biderman, Stella and Black, Sid and DiPofi, Anthony and Foster, Charles and Golding, Laurence and Hsu, Jeffrey and Le Noac'h, Alain and Li, Haonan and McDonell, Kyle and Muennighoff, Niklas and Ociepa, Chris and Phang, Jason and Reynolds, Laria and Schoelkopf, Hailey and Skowron, Aviya and Sutawika, Lintang...

    work page doi:10.5281/zenodo.12608602

  36. [36]

    GPT-4 Technical Report

    Gpt-4 technical report , author=. arXiv preprint arXiv:2303.08774 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  37. [37]

    LLaMA: Open and Efficient Foundation Language Models

    Llama: Open and efficient foundation language models , author=. arXiv preprint arXiv:2302.13971 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  38. [38]

    Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement

    Qwen2. 5-math technical report: Toward mathematical expert model via self-improvement , author=. arXiv preprint arXiv:2409.12122 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  39. [39]

    Qwen2.5-Coder Technical Report

    Qwen2. 5-coder technical report , author=. arXiv preprint arXiv:2409.12186 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  40. [40]

    2025 , eprint=

    Qwen2.5 Technical Report , author=. 2025 , eprint=

    2025

  41. [41]

    , author=

    Lora: Low-rank adaptation of large language models. , author=. ICLR , volume=

  42. [42]

    Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

    Outrageously large neural networks: The sparsely-gated mixture-of-experts layer , author=. arXiv preprint arXiv:1701.06538 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  43. [43]

    DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

    Deepseekmoe: Towards ultimate expert specialization in mixture-of-experts language models , author=. arXiv preprint arXiv:2401.06066 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  44. [44]

    arXiv preprint arXiv:2303.01610 , year=

    Sparse moe as the new dropout: Scaling dense and self-slimmable transformers , author=. arXiv preprint arXiv:2303.01610 , year=

    work page arXiv

  45. [45]

    Mixtral of Experts

    Mixtral of experts , author=. arXiv preprint arXiv:2401.04088 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  46. [46]

    IEEE Transactions on Knowledge and Data Engineering , year=

    A survey on mixture of experts in large language models , author=. IEEE Transactions on Knowledge and Data Engineering , year=