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arxiv: 2607.01792 · v1 · pith:7SG7EMH7new · submitted 2026-07-02 · 💻 cs.CL · cs.LG

PARTREP: Learning What to Repeat for Decoder-only LLMs

Andikawati P Widjaja , Yongjun Kim , Hyounghun Kim , Jaeho Lee This is my paper

Pith reviewed 2026-07-03 15:12 UTC · model grok-4.3

classification 💻 cs.CL cs.LG
keywords prompt repetitionKV cachedecoder-only LLMstoken selectionnegative log-likelihoodearly exitreasoning tasksselective augmentation
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The pith

Selective repetition of high-NLL tokens retains most gains from full prompt repetition while using 59 percent of the KV cache.

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

The paper shows that decoder-only LLMs suffer from uneven contextual grounding because causal attention gives later tokens more information than earlier ones. Full prompt repetition fixes this but doubles the KV cache and quadruples prefill cost. PartRep instead repeats only tokens that are hard to predict from context, identified by high negative log-likelihood, and uses a small gate on early hidden states to pick those tokens without a second full pass. If the selection works, the method delivers nearly the same accuracy lift on reasoning benchmarks at substantially lower memory and compute. A reader would care because it turns an otherwise impractical trick into something usable for longer inputs.

Core claim

PartRep appends only the tokens with highest token-wise negative log-likelihood to the prompt, where those tokens are chosen by a lightweight gate trained to predict them from early-layer hidden states via early exit. This selective repetition preserves most of the performance improvement of full prompt repetition across eight benchmarks and three model families while consuming 59.4 percent of the KV cache and 79.0 percent of the prefill FLOPs.

What carries the argument

A lightweight gate that predicts high negative log-likelihood tokens from early-layer hidden states to enable mid-prefill selection of tokens for repetition.

If this is right

  • Longer prompts become feasible because the added length is only a fraction of the original prompt rather than a full duplicate.
  • The same early-exit gate can be reused across different model sizes and families without retraining the base LLM.
  • Reasoning tasks that currently rely on full repetition, such as multi-step math or long-context retrieval, can run with lower memory overhead.
  • Prefill compute drops enough that the technique can be applied at inference time without dedicated hardware changes.

Where Pith is reading between the lines

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

  • The gate's early-layer features might also predict tokens worth caching or compressing in other efficiency methods.
  • If the selection criterion generalizes, similar lightweight predictors could decide which tokens to attend to more heavily during generation.
  • Testing the method on sequences much longer than the current benchmarks would reveal whether the savings scale linearly.

Load-bearing premise

High negative log-likelihood tokens are exactly the ones that gain the most from appearing again in a later position.

What would settle it

An ablation in which repeating the lowest-NLL tokens instead of the highest-NLL tokens produces equal or larger accuracy gains on the same benchmarks would show the selection signal does not work.

Figures

Figures reproduced from arXiv: 2607.01792 by Andikawati P Widjaja, Hyounghun Kim, Jaeho Lee, Yongjun Kim.

Figure 1
Figure 1. Figure 1: Overview of token repetition strategies. Vanilla prompting uses the original prompt once. Full repetition appends the entire prompt, improving accu￾racy at the cost of doubling the input length to 2L. PARTREP, our method, appends only selected key to￾kens, producing a shorter sequence of length (L + τL) that preserves the accuracy gains of repetition while reducing computational cost. ones. Although this d… view at source ↗
Figure 2
Figure 2. Figure 2: Inference procedure of the proposed PARTREP. We first prefill the LLM with the original prompt, then pass its early-layer hidden states through the gating module. Next, we select the top-τ fraction of tokens, and repeat the selected tokens (i.e., append it after the original prompt), then continue with the prefill. 3.1 Comparison with KV cache eviction Recall that the task of KV cache eviction is about dec… view at source ↗
Figure 3
Figure 3. Figure 3: Memory and compute scaling with prompt length. The panel (a) reports the number of stored KV cache tokens, and the panel (b) reports the estimated prefill FLOPs. As prompt length increases, Full Repeti￾tion incurs substantially larger overhead, whereas Partial Repetition maintains lower KV cache usage and prefill compute by appending only selected informative tokens. 6.2 Efficiency comparison [PITH_FULL_I… view at source ↗
Figure 4
Figure 4. Figure 4: Comparison of token scoring criteria un￾der different repetition scoring criteria. Selecting tokens with the highest NLL consistently yields strong performance, supporting NLL as an effective supervi￾sion signal for the gating module. As our method uses token-wise negative log￾likelihood (NLL) as the target importance signal for Connector Prompt Example Query Verbal "\nPay attention to these key tokens:\n{… view at source ↗
Figure 5
Figure 5. Figure 5: Effect of the early-exit layer used by the gating module. Accuracy improves when the gate re￾ceives sufficiently contextualized hidden states, whereas latency increases monotonically with extraction depth. We select layer 18 as the default operating point [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: Comparison of connector prompts used to append the selected tokens in ARC. A simple natural￾language instruction, “Pay attention to these key to￾kens:”, performs best and is used as the default connec￾tor. After selecting informative tokens, Partial Repeti￾tion appends them to the original prompt through a short connector string. We evaluate whether the form of this connector affects the usefulness of the … view at source ↗
Figure 6
Figure 6. Figure 6: Effect of the repetition ratio τ . Larger bud￾gets increase the KV-cache footprint, while accuracy is non-monotonic, suggesting that selectively repeating a compact set of informative tokens is preferable to indis￾criminately increasing the repeated context. We next examine the effect of the repetition budget τ , which controls the fraction of original prompt tokens appended by Partial Repetition. Increasi… view at source ↗
read the original abstract

While decoder-only LLMs excel at a vast array of natural language tasks, it suffers from an asymmetric information flow induced by causal attention: later tokens are richer in contextual grounding than earlier ones. A simple and effective remedy is prompt repetition -- just appending a second copy of prompt before generation can redistribute grounding across positions and improve reasoning performance. However, full repetition of the original prompt doubles the KV cache footprint and quadruples attention cost during prefill, making it impractical for long-context settings. We propose PartRep, a selective augmentation method that appends only the most informative tokens -- rather than the entire prompt. We use token-wise negative log-likelihood (NLL) as a selection signal, motivated by the hypothesis that less predictable tokens are less recoverable from surrounding context and therefore benefit more from late-position repetition. To avoid the heavy cost of a full forward pass for scoring, we train a lightweight gate that predicts high-NLL tokens from early-layer hidden states, enabling token selection during mid-prefill via early exit. Across eight benchmarks (including MMLU, GSM8K, and RULER) and three model families (Qwen2.5, Llama3.2, Gemma4), PartRep retains most of the gains of full repetition while using only 59.4\% of its KV cache and 79.0\% of its prefill FLOPs.

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 / 3 minor

Summary. The manuscript proposes PartRep, a method for selective prompt repetition in decoder-only LLMs to mitigate asymmetric information flow from causal attention. It selects tokens via token-wise negative log-likelihood (NLL) under the hypothesis that high-NLL tokens benefit most from late-position repetition, and uses a lightweight gate trained on early-layer hidden states to enable mid-prefill selection without a full forward pass. Across eight benchmarks (MMLU, GSM8K, RULER and others) and three model families (Qwen2.5, Llama3.2, Gemma4), the method is claimed to retain most performance gains of full repetition while using 59.4% of the KV cache and 79.0% of the prefill FLOPs.

Significance. If the empirical results hold under proper statistical controls, PartRep offers a practical route to improved reasoning performance in long-context settings at substantially lower memory and compute cost than full repetition. The multi-family, multi-benchmark evaluation and the early-exit gate design are concrete strengths that could influence efficient inference techniques.

major comments (2)
  1. [Abstract and experimental results] Abstract and experimental results sections: the central claim that PartRep 'retains most of the gains' at 59.4% KV cache and 79.0% prefill FLOPs is reported as point estimates with no error bars, standard deviations, or statistical significance tests across runs or datasets. This directly affects the reliability of the efficiency-performance tradeoff that constitutes the paper's primary contribution.
  2. [Method] Method section describing the NLL-based selection and lightweight gate: while the hypothesis is stated, there is no ablation comparing high-NLL selection against random or low-NLL baselines to isolate whether the NLL signal is load-bearing for the observed gains, or reporting the gate's prediction accuracy/F1 on held-out tokens.
minor comments (3)
  1. [Abstract and results] Clarify the precise operational definition of 'most of the gains' (e.g., what fraction of the full-repetition delta is considered sufficient) and report per-benchmark retention percentages rather than aggregate statements.
  2. [Experimental setup] Provide dataset sizes, number of evaluation examples per benchmark, and exact prompt lengths used when measuring KV-cache and FLOPs percentages, as these affect the reported efficiency numbers.
  3. [Abstract] The abstract lists 'Gemma4'; confirm the exact model variant and cite the corresponding paper or release note.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments and the recommendation for minor revision. We address the two major comments point by point below.

read point-by-point responses

  1. Referee: [Abstract and experimental results] Abstract and experimental results sections: the central claim that PartRep 'retains most of the gains' at 59.4% KV cache and 79.0% prefill FLOPs is reported as point estimates with no error bars, standard deviations, or statistical significance tests across runs or datasets. This directly affects the reliability of the efficiency-performance tradeoff that constitutes the paper's primary contribution.

    Authors: We agree that reporting variability would strengthen the primary efficiency claim. In the revision we will add standard deviations for all experiments that were run with multiple random seeds (GSM8K, MMLU subsets) and include paired statistical significance tests against the no-repetition baseline. For the remaining benchmarks we will report per-model consistency across the three families as additional robustness evidence. These changes will be reflected in both the abstract and the experimental results section. revision: yes

  2. Referee: [Method] Method section describing the NLL-based selection and lightweight gate: while the hypothesis is stated, there is no ablation comparing high-NLL selection against random or low-NLL baselines to isolate whether the NLL signal is load-bearing for the observed gains, or reporting the gate's prediction accuracy/F1 on held-out tokens.

    Authors: We will add the requested ablations to the revised Method and Experiments sections. Specifically, we will compare high-NLL selection against (i) random token selection at the same budget and (ii) low-NLL selection, measuring downstream accuracy on the eight benchmarks. We will also report the gate's token-level accuracy and F1 on a held-out validation split of the training data used to train the gate, together with the early-exit layer chosen. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper presents PartRep as an empirical method: NLL is used as a selection signal for tokens to repeat, a lightweight gate is trained to predict high-NLL tokens from early hidden states, and performance is measured directly on eight benchmarks across three model families. No equations, derivations, or predictions reduce the reported gains (or the cost savings of 59.4% KV cache / 79.0% prefill FLOPs) to quantities defined by the method itself. The motivating hypothesis is stated as a testable design choice rather than a self-referential premise, and results are framed as external measurements rather than outputs forced by fitting or self-citation. The derivation chain is therefore self-contained against the reported empirical evidence.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The approach rests on one domain assumption about NLL as a selection signal and introduces one new trained component; no free parameters are explicitly quantified in the abstract.

axioms (1)
  • domain assumption Less predictable tokens (high NLL) benefit more from late-position repetition because they are less recoverable from surrounding context
    Directly motivates the token selection signal in the abstract.
invented entities (1)
  • lightweight gate no independent evidence
    purpose: Predicts high-NLL tokens from early-layer hidden states to enable mid-prefill selection without full forward pass
    New component introduced to avoid heavy scoring cost

pith-pipeline@v0.9.1-grok · 5786 in / 1119 out tokens · 28002 ms · 2026-07-03T15:12:50.296880+00:00 · methodology

discussion (0)

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Reference graph

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