Towards Analyzing and Understanding the Limitations of VAPO: A Theoretical Perspective

• URL: http://arxiv.org/abs/2505.17997v2
• Date: Tue, 27 May 2025 08:30:19 GMT
• Title: Towards Analyzing and Understanding the Limitations of VAPO: A Theoretical Perspective
• Authors: Jintian Shao, Yiming Cheng, Hongyi Huang, Beiwen Zhang, Zhiyu Wu, You Shan, Mingkai Zheng,
• Abstract summary: VAPO is a framework for reinforcement learning for large language models.<n>It addresses challenges such as value model bias, heterogeneous sequence lengths, and sparse reward signals.<n>This paper explores VAPO from a theoretical perspective, highlighting areas where its assumptions might be challenged.
• Score: 6.963986923957048
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The VAPO framework has demonstrated significant empirical success in enhancing the efficiency and reliability of reinforcement learning for long chain-of-thought (CoT) reasoning tasks with large language models (LLMs). By systematically addressing challenges such as value model bias, heterogeneous sequence lengths, and sparse reward signals, VAPO achieves state-of-the-art performance. While its practical benefits are evident, a deeper theoretical understanding of its underlying mechanisms and potential limitations is crucial for guiding future advancements. This paper aims to initiate such a discussion by exploring VAPO from a theoretical perspective, highlighting areas where its assumptions might be challenged and where further investigation could yield more robust and generalizable reasoning agents. We delve into the intricacies of value function approximation in complex reasoning spaces, the optimality of adaptive advantage estimation, the impact of token-level optimization, and the enduring challenges of exploration and generalization.

Related papers

• CTRLS: Chain-of-Thought Reasoning via Latent State-Transition [57.51370433303236]
  Chain-of-thought (CoT) reasoning enables large language models to break down complex problems into interpretable intermediate steps.<n>We introduce groundingS, a framework that formulates CoT reasoning as a Markov decision process (MDP) with latent state transitions.<n>We show improvements in reasoning accuracy, diversity, and exploration efficiency across benchmark reasoning tasks.
  arXiv  Detail & Related papers  (2025-07-10T21:32:18Z)
• Towards Analyzing and Understanding the Limitations of VAPO: A Theoretical Perspective [0.0]
  Reinforcement learning (RL) enhances large language models (LLMs) in complex, long-chain-of-thought (long-CoT) reasoning.<n>We argue VAPO's limitations stem from inherent difficulties in credit assignment, value function representational capacity with temporally abstracted goals, and translating global value signals into local policy improvements.
  arXiv  Detail & Related papers  (2025-06-03T16:20:47Z)
• Supervised Optimism Correction: Be Confident When LLMs Are Sure [91.7459076316849]
  We establish a novel theoretical connection between supervised fine-tuning and offline reinforcement learning.<n>We show that the widely used beam search method suffers from unacceptable over-optimism.<n>We propose Supervised Optimism Correction, which introduces a simple yet effective auxiliary loss for token-level $Q$-value estimations.
  arXiv  Detail & Related papers  (2025-04-10T07:50:03Z)
• Trade-offs in Large Reasoning Models: An Empirical Analysis of Deliberative and Adaptive Reasoning over Foundational Capabilities [101.77467538102924]
  Recent advancements in Large Reasoning Models (LRMs) have demonstrated remarkable performance in specialized reasoning tasks.<n>We show that acquiring deliberative reasoning capabilities significantly reduces the foundational capabilities of LRMs.<n>We demonstrate that adaptive reasoning -- employing modes like Zero-Thinking, Less-Thinking, and Summary-Thinking -- can effectively alleviate these drawbacks.
  arXiv  Detail & Related papers  (2025-03-23T08:18:51Z)
• A Comprehensive Survey on Evidential Deep Learning and Its Applications [64.83473301188138]
  Evidential Deep Learning (EDL) provides reliable uncertainty estimation with minimal additional computation in a single forward pass. We first delve into the theoretical foundation of EDL, the subjective logic theory, and discuss its distinctions from other uncertainty estimation frameworks. We elaborate on its extensive applications across various machine learning paradigms and downstream tasks.
  arXiv  Detail & Related papers  (2024-09-07T05:55:06Z)
• Provable Representation with Efficient Planning for Partial Observable Reinforcement Learning [74.67655210734338]
  In most real-world reinforcement learning applications, state information is only partially observable, which breaks the Markov decision process assumption. We develop a representation-based perspective that leads to a coherent framework and tractable algorithmic approach for practical reinforcement learning from partial observations. We empirically demonstrate the proposed algorithm can surpass state-of-the-art performance with partial observations across various benchmarks.
  arXiv  Detail & Related papers  (2023-11-20T23:56:58Z)
• Constrained Bayesian Optimization with Adaptive Active Learning of Unknown Constraints [10.705151736050967]
  optimizing objectives under constraints is a common scenario in real-world applications such as scientific experimental design, design of medical therapies, and industrial process optimization. We propose an efficient CBO framework that intersects the ROIs identified from each aspect to determine the general ROI. We showcase the efficiency and robustness of our proposed CBO framework through empirical evidence and discuss the fundamental challenge of deriving practical regret bounds for CBO algorithms.
  arXiv  Detail & Related papers  (2023-10-12T22:32:00Z)
• Probabilistic Constrained Reinforcement Learning with Formal Interpretability [2.990411348977783]
  We propose a novel Adaptive Wasserstein Variational Optimization, namely AWaVO, to tackle these interpretability challenges. Our approach uses formal methods to achieve the interpretability for convergence guarantee, training transparency, and intrinsic decision-interpretation. In comparison with state-of-theart benchmarks including TRPO-IPO, PCPO and CRPO, we empirically verify that AWaVO offers a reasonable trade-off between high performance and sufficient interpretability.
  arXiv  Detail & Related papers  (2023-07-13T22:52:22Z)
• Advancing Counterfactual Inference through Nonlinear Quantile Regression [77.28323341329461]
  We propose a framework for efficient and effective counterfactual inference implemented with neural networks. The proposed approach enhances the capacity to generalize estimated counterfactual outcomes to unseen data. Empirical results conducted on multiple datasets offer compelling support for our theoretical assertions.
  arXiv  Detail & Related papers  (2023-06-09T08:30:51Z)
• Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning [24.09547181095033]
  Causal Graph is a structure built upon the relation between objects and events. We propose a framework with theoretical performance guarantees that alternates between two steps. Our performance improvement is attributed to the virtuous cycle of causal discovery, transition modeling, and policy training.
  arXiv  Detail & Related papers  (2022-07-19T05:31:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.