REVIEW 4 major objections 6 minor 6 cited by
RePO: Replay-Enhanced Policy Optimization
T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash
Pith's one-line read RePO, a replay-enhanced variant of GRPO, improves math-reasoning accuracy by up to 18.4 average points at roughly 15% extra compute.
desk verdict RePO is a clean method paper with a real confound: the final epoch doubles RePO's per-step samples relative to GRPO, so the headline gains aren't cleanly attributable to replay until a same-sample-count control is run. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The load-bearing object is the replay buffer plus a replay strategy. The buffer stores, for each prompt, previously sampled completions together with their generation probabilities under the behavior policy; the off-policy update term $J_{\mathrm{off}}(\theta)$ applies the same clipped GRPO-style loss to a replayed group, with the importance ratio $r^{\mathrm{off}}_{i,t} = \pi_\theta(o^{\mathrm{off}}_{i,t} \mid q, o^{\mathrm{off}}_{i,<t}) / \pi_{\theta_{\mathrm{off}}}(o^{\mathrm{off}}_{i,t} \mid q, o^{\mathrm{off}}_{i,<t})$ downweighting samples the current policy no longer assigns high probability. Four strategies are proposed: Full-scope (all past samples), Recency-based (most recent $K$), Reward-oriented (highest reward), and Variance-driven (highest reward variance, aimed at vanishing-gradient decay). The paper also separates advantage normalization between on- and off-policy groups (the 'Split' strategy), which is shown to beat mixing the two groups.
What would settle it
Run GRPO on Qwen2.5-Math-1.5B for three epochs with 64 training examples per step in the final epoch, keeping all other hyperparameters identical to the paper's setup, and evaluate on the same seven math benchmarks. If the GRPO baseline reaches the same ~35.8 average accuracy as RePO, the reported gains can be explained by the larger per-step data size rather than the replay mechanism.
Extended reading notes
Core claim
The paper's central claim is that an off-policy replay term, added to the GRPO objective, improves policy optimization for mathematical reasoning without a large compute penalty. For each prompt, RePO stores sampled outputs with their generation probabilities in a replay buffer; at update time it retrieves a group of these old samples using a chosen replay strategy and optimizes the current policy on both the fresh on-policy group and the replayed off-policy group, estimating advantages separately for the two groups. Across five Qwen models and seven math benchmarks, this consistently beats plain GRPO, with the largest gains on the smallest base model (18.4 average points) and with the number of effective optimization steps rising by 48% for Qwen3-1.7B when both on- and off-policy samples are set to 8. The authors attribute the gains to increased sample diversity and to the rescue of gradient signal when a GRPO step would otherwise see identical rewards and thus zero advantages.
Load-bearing premise
The comparison attributes the gains to replay itself, yet RePO's final training epoch uses 64 examples per optimization step while GRPO keeps 32, and no GRPO baseline with 64 examples per step (or an equivalent extra epoch) is reported.
Editorial extensions
If this is right
- If RePO is correct, GRPO-style RL for reasoning models can reuse past rollouts to make each prompt's samples contribute more signal, reducing the data and compute needed for the same accuracy.
- The 48% increase in effective optimization steps suggests replay directly counters the degenerate all-reward-equal steps that give GRPO zero gradient, a known failure mode during training.
- The method transfers to at least one GRPO variant besides the base algorithm: RePO (Dr. GRPO) beats Dr. GRPO by 1.3 and 4.2 average points on two models, so replay appears to be a plug-in ingredient.
- Because the best replay strategy differs between base and instruct models, practitioners can tune replay selection per model; the paper's fixed choices (Recency-based for base, Reward-oriented for instruct) are a simple default.
- The 15% extra compute buys more than 15% in effective optimization, implying replay is a compute-efficient way to spend an RL budget.
Reading between the lines
- Editorial inference: RePO's replay mechanism could be applied to non-mathematical RL settings—code generation, tool use, instruction following—where reward signals are similarly sparse and fresh rollouts are expensive.
- Editorial inference: The variance-driven strategy is designed for reward collapse, so a testable extension is to use it with learned reward models whose scores are noisy, where a larger group variance signals useful gradient information.
- Editorial inference: The separate (Split) advantage estimation could be combined with an adaptive replay ratio that gradually shifts from recency-based to reward-oriented selection as training progresses, which the model-dependent results in Table 5 suggest might help.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper introduces Replay-Enhanced Policy Optimization (RePO), an extension of GRPO that adds an off-policy replay-buffer loss to the standard on-policy GRPO objective. The off-policy term retrieves previously stored samples according to one of several replay strategies (full-scope, recency-based, reward-oriented, variance-driven) and computes separately normalized advantages for on- and off-policy groups. Experiments on five Qwen models across seven math benchmarks report average gains of up to 18.4 points over GRPO, with about 15% additional compute, plus smaller gains on general reasoning benchmarks. The paper also reports comparisons with Dr. GRPO, ablations of replay strategy and advantage-estimation variant, and an analysis of 'effective optimization steps.'
Significance. If the reported gains are genuinely caused by replaying off-policy samples, RePO would be a simple and practically useful modification of GRPO that improves data efficiency at modest cost. The manuscript is clearly written, the method is precisely specified with pseudocode and a release of code, and the gradient-ratio interpretation in Section 3.2 is a useful conceptual contribution. The replay-strategy ablations (Table 5) and the separate-advantage analysis (Table 4) provide informative design evidence. However, the central experimental claim currently rests on a comparison that confounds replay with an increase in per-step data and optimization signal, and on single-run results without variance estimates, so the magnitude and attribution of the headline gains are not yet established.
major comments (4)
- [Appendix A.1 / Section 4.2] The main claim that replay improves GRPO is not supported by the current experimental design because RePO and GRPO differ in the final epoch in two coupled ways. Appendix A.1 states that GRPO uses 32 training examples per step in all epochs, while RePO increases to 64 examples per step in the last epoch (8 on-policy and 8 off-policy per prompt), with the off-policy loss active only in that epoch. No GRPO control with 64 examples per step or with an additional epoch is reported, so the gains in Table 1 may be due to the larger number of final-epoch samples and optimization signal rather than to the replay mechanism itself. Please add an equal-sample-count GRPO baseline, or a RePO ablation that uses 64 on-policy samples per step in the final epoch without the replay loss, to isolate the effect of replay.
- [Section 4.2 / Tables 1 and 3] All reported results are single point estimates with no seeds, confidence intervals, or significance tests. Several of the claimed improvements are very small (for example, Qwen2.5-Math-1.5B-Instruct average 45.8 vs. 46.1 in Table 1, and gains of 0.2–2.4 points in Table 3), and the paper makes strong claims of consistency ('consistently outperforms') on the strength of these differences. Please report results over at least three seeds with standard deviations for the main comparisons, or explicitly state if single runs were used and soften the claims accordingly.
- [Section 4.4, Table 7, and footnote 1] The 'effective-step' analysis used to explain RePO's effectiveness is computed at a unit that does not match the training procedure. Footnote 1 assumes 'each step consists of a single prompt,' but Algorithm 1 samples a batch of 32 prompts per step and takes optimizer steps over that batch; the condition that all rewards in a group are equal is evaluated per prompt, not per batch, and the relevant quantity for whether a gradient step is 'effective' is the batch-level gradient, not the per-prompt advantage. The 47.8% relative increase in effective steps therefore does not directly support the stated mechanism. Please either redefine the metric at the batch level used in training or present direct evidence (e.g., measured fraction of near-zero batch gradients) that RePO's advantage survives the batch averaging.
- [Section 4.3 and Table 5] The replay strategy used in the headline results of Table 1 is selected per model class (Recency-based for base models, Reward-oriented for instruct models) based on the same seven math benchmarks reported in Table 5. Because the selection and the evaluation share the same test sets, the reported average gains in Table 1 carry an optimism bias from model selection over the replay-strategy choices. This does not invalidate the method, but the paper should either report results for a strategy chosen before seeing the test benchmarks, or present the full Table 5 as the primary comparison and treat Table 1 as a selected-configuration summary, with an explicit caveat about selection on the evaluation set.
minor comments (6)
- [Section 4.1] The metric 'avg@32' for AIME24, AIME25, and AMC is not defined in the main text or appendix; please clarify whether it is a pass@32 estimate or a different aggregation, and state how the 32 samples are generated.
- [Table 6] The 'Rel. Time' column reports values normalized to GRPO with 4 on-policy samples, but the caption does not explain why GRPO with 8 on-policy samples is shown as ×2.02; please state the absolute training time or the precise recipe for the normalization.
- [Section 3.2 and Table 4] The notation in Table 4 labels rows as 'MIXED' and 'SPLIT,' while Section 3.2 always uses 'Split' in prose; please harmonize the capitalization. Additionally, the mixed-strategy objective is not written down; including its equation would help reproducibility.
- [Section 4.4, Figure 2] The x-axis 'Number of Replay Samples' is introduced without clarifying whether it is the number of off-policy samples per prompt (G_off) or the total replay-buffer size; the text and Algorithm 1 suggest the former, but the figure should state this explicitly.
- [Appendix A.1] The statement that 'the off-policy update is applied only in the final epoch' means that E_off in Algorithm 1 is set to N; this should be named explicitly in the algorithm description rather than left implicit.
- [General] The paper references prior off-policy RL work in the LLM literature (e.g., LUFFY) but does not discuss off-policy correction beyond the importance ratio; a brief mention of how the clipping and separate advantage normalization relate to standard off-policy policy-gradient corrections would improve the positioning.
Circularity Check
No significant circularity: RePO's replay loss is defined independently of its results and its gains are empirical against external benchmarks.
full rationale
RePO's derivation chain does not reduce to its inputs. The proposed loss in Section 3 combines a standard GRPO-style on-policy term with an off-policy importance-ratio term; the gradient comparison in Section 3.2 is an algebraic identity relating the two defined losses, not a fitted quantity renamed as a prediction. The effectiveness claims are empirical comparisons against GRPO on external math and general reasoning benchmarks. The missing GRPO control with 64 samples per step in the final epoch (Appendix A.1) is an experimental-attribution weakness, not a circularity, because RePO's advantage is not obtained by fitting a parameter to the evaluation data. The choice of replay strategy per model from Table 5 before presenting Table 1 is a model-selection practice; the final results are still measured against the same external benchmarks and are not constructed to equal a fitted quantity. The 'effective-step' analysis in Table 7 uses a single-prompt definition of step that differs from the batched training setup, but this is a measurement inconsistency rather than a circular reduction. No load-bearing argument rests on a self-citation chain: the cited GRPO, Dr. GRPO, and vanishing-gradient works are external and are not used to define RePO's outcome. Overall, the paper's central claim has independent empirical content, so the circularity score is 0.
Assumptions & free parameters
free parameters (4)
- off-policy sample count K =
8
- replay strategy per model =
Recency for base models, Reward-oriented for instruct models
- off-policy start epoch =
Epoch 3 of 3
- off-policy loss weight =
1.0 (implicit)
assumptions (4)
- standard math Importance-sampling ratio pi_theta / pi_theta_off is a valid correction for training on off-policy samples with clipping.
- domain assumption The Math-Verify reward function provides a reliable training signal where 1 is correct and 0 is incorrect.
- domain assumption Pass@1 and avg@32 scores across the seven benchmarks can be averaged into a single comparable average.
- ad hoc to paper A step in the effective-step analysis corresponds to a single prompt.
Cite this review
Pith. "Pith review of RePO: Replay-Enhanced Policy Optimization." pith.science (2026). https://pith.science/paper/FDDXSKF6
@misc{pith2026250609340,
author = {Pith},
title = {Pith review of: RePO: Replay-Enhanced Policy Optimization},
year = {2026},
howpublished = {\url{https://pith.science/paper/FDDXSKF6}},
note = {Machine review of arXiv:2506.09340}
}
abstract
Reinforcement learning (RL) is vital for optimizing large language models (LLMs). Recent Group Relative Policy Optimization (GRPO) estimates advantages using multiple on-policy outputs per prompt, leading to high computational costs and low data efficiency. To address this, we introduce Replay-Enhanced Policy Optimization (RePO), which leverages diverse replay strategies to retrieve off-policy samples from a replay buffer, allowing policy optimization based on a broader and more diverse set of samples for each prompt. Experiments on five LLMs across seven mathematical reasoning benchmarks demonstrate that RePO achieves absolute average performance gains of $18.4$ and $4.1$ points for Qwen2.5-Math-1.5B and Qwen3-1.7B, respectively, compared to GRPO. Further analysis indicates that RePO increases computational cost by $15\%$ while raising the number of effective optimization steps by $48\%$ for Qwen3-1.7B, with both on-policy and off-policy sample numbers set to $8$. The repository can be accessed at https://github.com/SihengLi99/RePO.
Figures
Forward citations
Cited by 6 Pith papers
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Experience Augmented Policy Optimization for LLM Reasoning
EAPO reuses prior RL policy experience adaptively at decision points in LLM rollouts with adapted importance sampling and reports gains over prior RLVR methods on math benchmarks.
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Beyond Normalization: Rethinking the Partition Function as a Difficulty Scheduler for RLVR
Using the GFlowNet partition function as an online accuracy estimate for difficulty-based prompt selection and replay improves sample efficiency of RLVR for LLM reasoning.
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Squeeze the Soaked Sponge: Efficient Off-policy Reinforcement Finetuning for Large Language Model
ReMix cuts reinforcement finetuning rollout volume by 30x to 450x on math reasoning by mixing historical and on-policy data with a convex KL constraint and a mid-training switch to on-policy updates.
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ARMOR: Stabilizing On-Policy LLM RL with Off-Policy Anchor Samples
Injecting correct reference-policy samples and optimizing a mixed importance-sampling ratio prevents validation collapse and raises asymptotic math-reasoning scores beyond reverse-KL baselines.
-
RSPO: Reward-Swap Policy Optimization for Multi-Turn LLM Agents
Reward-Swap Policy Optimization uses dense-reward exploration to generate diverse trajectories that then train the final policy strictly under true outcome rewards, lifting multi-turn agent performance.
-
AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning
AutoTool's two-phase SFT/RL plus ranking training lets 8B LLM agents beat larger fixed-tool agents across math, search, code, and vision benchmarks, though unseen-tool gains are asserted, not isolated.
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Reviewed August 7, 2026 · model on record in the stance chip above.
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