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arxiv: 2504.05118 · v3 · submitted 2025-04-07 · 💻 cs.AI

Recognition: 2 theorem links

· Lean Theorem

VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks

Yu Yue , Yufeng Yuan , Qiying Yu , Xiaochen Zuo , Ruofei Zhu , Wenyuan Xu , Jiaze Chen , Chengyi Wang
show 19 more authors
Tiantian Fan Zhengyin Du Xiangpeng Wei Xiangyu Yu Gaohong Liu Juncai Liu Lingjun Liu Haibin Lin Zhiqi Lin Bole Ma Chi Zhang Mofan Zhang Wang Zhang Hang Zhu Ru Zhang Xin Liu Mingxuan Wang Yonghui Wu Lin Yan
Authors on Pith no claims yet

Pith reviewed 2026-05-13 09:30 UTC · model grok-4.3

classification 💻 cs.AI
keywords VAPOvalue-based reinforcement learninglong chain-of-thoughtreasoning modelsAIME 2024proximal policy optimizationtraining stabilitysparse rewards
0
0 comments X

The pith

VAPO reaches 60.4 on AIME 2024 by fixing value bias, variable lengths, and sparse rewards in RL for reasoning.

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

The paper introduces VAPO, a value-based augmented proximal policy optimization framework for training large reasoning models on long chain-of-thought tasks. It targets three persistent issues in value-based reinforcement learning: bias in the value model, sequences that vary in length, and rewards that appear only rarely. By integrating targeted fixes for these issues, VAPO delivers higher accuracy on hard math reasoning benchmarks while training faster and without the crashes common in prior runs. A reader would care because reliable training of advanced reasoning could reduce the compute and instability barriers that currently limit such models.

Core claim

VAPO provides an integrated solution to value model bias, heterogeneous sequence lengths, and reward sparsity in long-CoT reasoning. Built on the Qwen 32B model, the framework attains a score of 60.4 on the AIME 2024 dataset, outperforming prior reported results for DeepSeek-R1-Zero-Qwen-32B and DAPO by more than 10 points under identical settings. It reaches this performance in only 5000 training steps and maintains stability with no crashes across multiple independent runs.

What carries the argument

The VAPO framework, which augments proximal policy optimization with value-based components to mitigate bias, length heterogeneity, and reward sparsity during reasoning model training.

Where Pith is reading between the lines

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

  • The same fixes for bias and sparsity could be tested on non-math reasoning domains such as code generation or scientific question answering.
  • If stability scales with model size, value-based methods might become the default for long-horizon language-model training where crashes currently waste compute.
  • The 5000-step convergence suggests future experiments could measure wall-clock time or total tokens processed to quantify efficiency gains beyond step count.

Load-bearing premise

The performance and stability gains come from the specific VAPO design choices rather than unreported differences in data, hyperparameters, model initialization, or evaluation protocols.

What would settle it

Reproduce the AIME 2024 experiments using identical training data, hyperparameters, model initialization, and evaluation code to verify whether the 10-point margin and zero-crash stability still appear.

read the original abstract

We present VAPO, Value-based Augmented Proximal Policy Optimization framework for reasoning models., a novel framework tailored for reasoning models within the value-based paradigm. Benchmarked the AIME 2024 dataset, VAPO, built on the Qwen 32B pre-trained model, attains a state-of-the-art score of $\mathbf{60.4}$. In direct comparison under identical experimental settings, VAPO outperforms the previously reported results of DeepSeek-R1-Zero-Qwen-32B and DAPO by more than 10 points. The training process of VAPO stands out for its stability and efficiency. It reaches state-of-the-art performance within a mere 5,000 steps. Moreover, across multiple independent runs, no training crashes occur, underscoring its reliability. This research delves into long chain-of-thought (long-CoT) reasoning using a value-based reinforcement learning framework. We pinpoint three key challenges that plague value-based methods: value model bias, the presence of heterogeneous sequence lengths, and the sparsity of reward signals. Through systematic design, VAPO offers an integrated solution that effectively alleviates these challenges, enabling enhanced performance in long-CoT reasoning tasks.

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 / 1 minor

Summary. The paper introduces VAPO, a value-based augmented proximal policy optimization framework for long chain-of-thought reasoning in large language models. Built on Qwen-32B, it reports a state-of-the-art score of 60.4 on AIME 2024, outperforming DeepSeek-R1-Zero-Qwen-32B and DAPO by more than 10 points under identical experimental settings. The work highlights three challenges in value-based RL (value model bias, heterogeneous sequence lengths, reward sparsity) and claims an integrated design that yields stable, efficient training reaching SOTA performance in 5,000 steps with no crashes across multiple runs.

Significance. If the reported gains and stability are reproducible under truly matched conditions, the result would be significant for reliable RL-based reasoning, as it directly targets load-bearing issues in value-based methods for long-CoT tasks and demonstrates practical efficiency on a 32B model.

major comments (2)
  1. [Abstract] Abstract: The central claim that VAPO outperforms DeepSeek-R1-Zero-Qwen-32B and DAPO by >10 points 'under identical experimental settings' is load-bearing for the performance contribution, yet the manuscript provides no side-by-side hyperparameter table, data-mixture citation, or reproduction protocol that verifies exact matching of initialization, optimizer schedule, reward shaping, length filtering, and evaluation protocol with the cited baselines.
  2. [Methods] Methods (implied by abstract description): The integrated solutions for value-model bias, heterogeneous lengths, and reward sparsity are presented at a high level without quantitative ablations or controlled experiments isolating each component's contribution to the 60.4 score and crash-free training; this weakens attribution of the stability and efficiency gains specifically to VAPO design choices.
minor comments (1)
  1. [Abstract] Abstract: The phrasing 'Benchmarked the AIME 2024 dataset' is grammatically incomplete and should be revised for clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for your thorough review of our manuscript. We appreciate the feedback on clarifying the experimental comparisons and providing more detailed ablations. We address each major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that VAPO outperforms DeepSeek-R1-Zero-Qwen-32B and DAPO by >10 points 'under identical experimental settings' is load-bearing for the performance contribution, yet the manuscript provides no side-by-side hyperparameter table, data-mixture citation, or reproduction protocol that verifies exact matching of initialization, optimizer schedule, reward shaping, length filtering, and evaluation protocol with the cited baselines.

    Authors: We acknowledge the importance of verifiable identical settings. The comparisons were conducted by strictly following the hyperparameter settings, data mixtures, and protocols as described in the original DeepSeek-R1-Zero and DAPO papers. To address this, we will include a detailed side-by-side hyperparameter table in the revised manuscript, citing specific sections from the baseline papers for initialization, optimizer schedule, reward shaping, length filtering, and evaluation. Additionally, we plan to release our full training code and scripts to facilitate exact reproduction. revision: yes

  2. Referee: [Methods] Methods (implied by abstract description): The integrated solutions for value-model bias, heterogeneous lengths, and reward sparsity are presented at a high level without quantitative ablations or controlled experiments isolating each component's contribution to the 60.4 score and crash-free training; this weakens attribution of the stability and efficiency gains specifically to VAPO design choices.

    Authors: We agree that quantitative ablations would better isolate the contributions of each component. In the revised manuscript, we will add a new section with controlled ablation experiments. These will include variants where we disable the value bias correction, the heterogeneous length handling, and the reward sparsity mitigation one at a time, reporting the resulting performance on AIME 2024 and training stability metrics (e.g., crash rates and convergence speed). This will provide direct evidence for the impact of each design choice. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical benchmark results with independent design claims

full rationale

The paper presents VAPO as an integrated RL framework addressing value-model bias, heterogeneous lengths, and reward sparsity in long-CoT reasoning. Central claims consist of empirical AIME 2024 benchmarks (60.4 score, >10-point gains over DeepSeek-R1-Zero-Qwen-32B and DAPO under identical settings, stable 5k-step training with no crashes). No equations, predictions, or first-principles derivations are shown that reduce by construction to fitted inputs, self-citations, or ansatzes. The work is self-contained as an empirical contribution; design choices are described as systematic without load-bearing self-referential loops or renamed known results.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no identifiable free parameters, axioms, or invented entities; the work is presented as an empirical engineering contribution rather than a theoretical derivation.

pith-pipeline@v0.9.0 · 5605 in / 1074 out tokens · 43570 ms · 2026-05-13T09:30:15.431236+00:00 · methodology

discussion (0)

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Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

  • IndisputableMonolith.Foundation.DAlembert.Inevitability bilinear_family_forced unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    VAPO, built on the Qwen 32B pre-trained model, attains a state-of-the-art score of 60.4. In direct comparison under identical experimental settings, VAPO outperforms the previously reported results of DeepSeek-R1-Zero-Qwen-32B and DAPO by more than 10 points.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Forward citations

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