EffiPair: Improving the Efficiency of LLM-generated Code with Relative Contrastive Feedback

Samira Hajizadeh; Suman Jana

arxiv: 2604.05137 · v1 · submitted 2026-04-06 · 💻 cs.PL · cs.AI· cs.CL· cs.LG· cs.SE

EffiPair: Improving the Efficiency of LLM-generated Code with Relative Contrastive Feedback

Samira Hajizadeh , Suman Jana This is my paper

Pith reviewed 2026-05-10 18:54 UTC · model grok-4.3

classification 💻 cs.PL cs.AIcs.CLcs.LGcs.SE

keywords LLM code generationcode efficiencyrelative feedbackinference-time refinementprogram pairscontrastive summariesruntime optimization

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The pith

Comparing pairs of similar programs lets LLMs refine generated code for better efficiency using lighter feedback than single-program profiling.

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

Large language models often output code that is correct yet slow or memory-heavy. Prior fixes relied on absolute measurements like one program's runtime, which proved expensive and gave unclear signals for change. This work instead generates multiple candidate solutions, selects pairs with big efficiency differences, and condenses the execution contrasts into short summaries. These summaries are fed back to the model at inference time to steer the next round of generation. The resulting framework improves speed while cutting token costs sharply and avoiding any model retraining.

Core claim

EffiPair introduces Relative Contrastive Feedback that compares two structurally similar programs for the same task, identifies differences tied to better efficiency, and turns those into lightweight signals. The iterative process of candidate generation, pair selection, summarization, and refinement replaces isolated scalar feedback, delivering up to 1.5x speedup over plain generation and more than 90% token reduction versus earlier methods while preserving functional correctness.

What carries the argument

Relative Contrastive Feedback (RCF), which selects program pairs with large efficiency gaps and summarizes their execution differences into direct, actionable guidance for the LLM.

If this is right

Iterative refinement at test time can improve both runtime and memory use of LLM code while keeping correctness intact.
Pairwise contrastive summaries reduce the need for repeated full profiling and long prompts compared to absolute feedback methods.
The approach works across different base LLMs without requiring fine-tuning or additional training data.
Token usage drops by more than 90 percent relative to prior refinement techniques.

Where Pith is reading between the lines

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

The same contrastive-pair idea could be tested on other optimization goals such as code size or security properties.
Success depends on the quality of the efficiency-difference summaries; noisy or incomplete summaries would weaken the guidance.
Embedding this feedback loop inside everyday code-completion tools might shift default outputs toward efficient code rather than post-hoc fixes.

Load-bearing premise

That summaries of efficiency differences between two similar programs will give the LLM sufficiently clear and usable instructions to produce faster or leaner code on its own.

What would settle it

Experiments on the same code-efficiency benchmarks in which applying the pairwise contrastive summaries produces no measurable speedup or higher token counts than generating code without any performance feedback.

Figures

Figures reproduced from arXiv: 2604.05137 by Samira Hajizadeh, Suman Jana.

**Figure 1.** Figure 1: Overview of EFFIPAIR. Given a coding task, the LLM samples N candidate programs. Candidates are checked for correctness and profiled for efficiency, then stored in a candidate pool. EFFIPAIR selects a pair consisting of an efficient reference program p + and a similar but less efficient candidate p −, summarizes their relative execution differences into compact Relative Contrastive Feedback (RCF), and uses… view at source ↗

**Figure 2.** Figure 2: Iterative refinement behavior of EFFIPAIR on EvalPerf across evaluated models. 5 Discussion and Limitations The main advantage of EFFIPAIR is not more profiling data, but better feedback. By contrasting an efficient candidate with a similar slower one, it gives the model a clear, actionable signal about what to change, making refinement more targeted than single-candidate prompting. Our results also sugge… view at source ↗

**Figure 3.** Figure 3: DPS, DPSnorm, and Pass@1 for various embedding weights [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗

**Figure 4.** Figure 4: DPS, DPSnorm, and Pass@1 for various similarity thresholds. our current configuration uses the wall-clock time on 3 measured runs. The reported elapsed time is the arithmetic mean over successful measured runs. Round-one correctness is computed over the outputs from an initial generation pass. After each iteration, the system updates the candidate pool and writes per-round artifacts, including round statis… view at source ↗

read the original abstract

Large language models (LLMs) often generate code that is functionally correct but inefficient in runtime and memory. Prior approaches to improving code efficiency typically rely on absolute execution feedback, such as profiling a single program's runtime or memory usage, which is costly and provides weak guidance for refinement. We propose Relative Contrastive Feedback (RCF), an inference-time feedback mechanism that requires no model fine-tuning or parameter updates. RCF compares two structurally similar programs for the same task and highlights the differences associated with better efficiency. Building on this idea, we introduce EffiPair, an inference-time iterative refinement framework that operates entirely at test time by generating multiple candidate solutions, identifying informative program pairs with large efficiency gaps, summarizing their execution differences into lightweight feedback, and using this signal to produce more efficient solutions. By replacing isolated scalar feedback with pairwise contrastive comparisons, EffiPair provides more direct guidance while reducing profiling and prompting overhead. Experiments on code-efficiency benchmarks show that EffiPair consistently improves efficiency while preserving correctness. For instance, with DeepSeek-Chat V3.2, EffiPair achieves up to 1.5x speedup over generation without performance feedback, while reducing token usage by more than 90% compared to prior work.

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.

Desk Editor's Note private letter to a colleague

EffiPair introduces relative contrastive feedback for inference-time LLM code refinement but the reported gains do not yet isolate whether the pairwise summaries outperform simpler absolute feedback.

read the letter

The main thing to know is that this paper describes an inference-time loop called EffiPair that generates code candidates, finds pairs with large efficiency gaps, summarizes the differences in a contrastive way, and feeds that back to the LLM to produce faster or lighter code without any fine-tuning or extra training data. It reports up to 1.5x speedup on DeepSeek-Chat V3.2 versus plain generation and over 90% token reduction versus prior absolute-feedback methods, while keeping functional correctness intact on code-efficiency benchmarks. The core mechanism replaces isolated scalar numbers with lightweight pairwise difference summaries, which the authors argue gives more direct guidance and lowers profiling cost. That framing is the actual novelty here, and the description of the iterative process is clear enough to follow. The experiments appear to show consistent efficiency lifts across the tested models and tasks, which is the practical contribution if the numbers replicate. The main gap is the missing ablation that would hold candidate generation, iteration count, and prompting budget fixed while swapping the contrastive summaries for direct absolute metrics like raw runtime or memory in the same loop. Without that comparison, it is hard to tell whether the relative format itself drives the improvement or whether any execution signal would produce similar results. The paper is aimed at researchers working on deployed LLM code tools who want low-overhead ways to cut runtime costs. It has a concrete proposal and some empirical backing, so it deserves a serious referee even though the controls will need tightening in revision. I would send it for peer review.

Referee Report

2 major / 1 minor

Summary. The paper proposes Relative Contrastive Feedback (RCF), an inference-time mechanism that compares pairs of structurally similar LLM-generated programs for the same task and summarizes efficiency differences (runtime and memory) into lightweight contrastive feedback. Building on this, it introduces the EffiPair iterative refinement framework that generates candidates, selects informative pairs with large efficiency gaps, and uses the summaries to guide further generations without fine-tuning or additional training data. The central claim is that this yields consistent efficiency gains on code-efficiency benchmarks while preserving correctness, with an example of up to 1.5x speedup over no-feedback generation using DeepSeek-Chat V3.2 and >90% token reduction versus prior work.

Significance. If the results hold under proper controls, the work would be significant for inference-time optimization of LLM code generation: it replaces costly absolute profiling with relative contrastive signals that the authors argue provide more direct guidance, while cutting prompting overhead. This could influence training-free refinement techniques in the field, especially if the contrastive mechanism proves uniquely effective.

major comments (2)

[Experimental Evaluation] Experimental Evaluation section: The abstract states specific quantitative claims (1.5x speedup with DeepSeek-Chat V3.2; >90% token reduction vs. prior work) but supplies no details on the benchmarks used, number of tasks, baselines, number of runs, variance, or statistical tests. This information is load-bearing for assessing whether the reported gains are robust.
[Framework and Ablation Studies] Framework and Ablation Studies: The central claim requires that pairwise contrastive summaries supply uniquely direct, actionable efficiency guidance that scalar absolute feedback cannot. However, no controlled ablation holds candidate generation, iteration count, and prompting budget fixed while swapping contrastive summaries for direct absolute profiling numbers (runtime + memory). Gains could therefore arise from any execution signal rather than the relative mechanism.

minor comments (1)

[Abstract] Abstract: The claim of 'reducing token usage by more than 90% compared to prior work' does not name the prior work or report the absolute token counts, making the comparison difficult to interpret.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments and the recommendation for major revision. We address each major comment point by point below, providing clarifications from the manuscript and committing to targeted revisions to strengthen the experimental rigor and framework analysis.

read point-by-point responses

Referee: Experimental Evaluation section: The abstract states specific quantitative claims (1.5x speedup with DeepSeek-Chat V3.2; >90% token reduction vs. prior work) but supplies no details on the benchmarks used, number of tasks, baselines, number of runs, variance, or statistical tests. This information is load-bearing for assessing whether the reported gains are robust.

Authors: We agree that the abstract's quantitative claims require explicit supporting details for robustness assessment. The Experimental Evaluation section (Section 4) describes the benchmarks (HumanEval, MBPP, and additional code-efficiency suites), the number of tasks (over 200 across datasets), and the baselines (no-feedback generation plus prior absolute-feedback methods). However, reporting of run counts, variance, and statistical tests is not as explicit as needed. In the revised manuscript, we will add these details: results averaged over 5 independent runs with standard deviations, and p-values from paired statistical tests (e.g., Wilcoxon signed-rank) to confirm significance. These will be incorporated into the tables and text of Section 4, with cross-references from the abstract. revision: yes
Referee: Framework and Ablation Studies: The central claim requires that pairwise contrastive summaries supply uniquely direct, actionable efficiency guidance that scalar absolute feedback cannot. However, no controlled ablation holds candidate generation, iteration count, and prompting budget fixed while swapping contrastive summaries for direct absolute profiling numbers (runtime + memory). Gains could therefore arise from any execution signal rather than the relative mechanism.

Authors: This is a fair critique of the need to isolate the relative contrastive mechanism. Our current experiments compare EffiPair against no-feedback baselines and prior absolute-feedback approaches, but these do not strictly fix candidate generation, iteration count, and prompting budget while swapping only the feedback type. We will add a new controlled ablation study in the revised manuscript. This study will hold the number of candidates, iterations, and total token budget constant, then directly compare prompts using relative contrastive summaries versus prompts that include absolute runtime and memory values. Results will be reported in a new table or subsection to demonstrate whether the relative format provides uniquely actionable guidance. revision: yes

Circularity Check

0 steps flagged

No circularity in the proposed inference-time framework

full rationale

The paper describes an empirical method (EffiPair with RCF) for iterative LLM code refinement at test time. No equations, derivations, first-principles results, or predictions appear in the text. The framework is explicitly positioned as distinct from prior absolute-feedback work, with no self-referential definitions, fitted parameters renamed as outputs, or load-bearing self-citations that reduce the central claim to its inputs. All reported gains (speedup, token reduction) are measured against external baselines via benchmark experiments, not by construction from the method's own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review reveals no explicit free parameters, axioms, or invented entities; the framework is presented as a procedural combination of existing LLM generation and profiling steps.

pith-pipeline@v0.9.0 · 5527 in / 1037 out tokens · 36798 ms · 2026-05-10T18:54:44.719032+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

4 extracted references · 4 canonical work pages

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