Adaptive Action Chunking via Multi-Chunk Q Value Estimation

• URL: http://arxiv.org/abs/2605.10044v1
• Date: Mon, 11 May 2026 06:14:32 GMT
• Title: Adaptive Action Chunking via Multi-Chunk Q Value Estimation
• Abstract summary: We propose Adaptive Action CHunking (ACH), a novel offline-to-online reinforcement learning algorithm.<n>ACH dynamically modulates chunk length during both training and inference.<n>Our mechanism allows the agent to select the most effective chunk length adaptively based on the current state.
• Score: 18.426163168939365
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Action chunking emerged as a pivotal technique in imitation learning, enabling policies to predict cohesive action sequences rather than single actions. Recently, this approach has expanded to reinforcement learning (RL), enhancing behavioral consistency and reducing bootstrapping errors in value function estimation. However, existing methods rely on a fixed chunk length, creating a performance bottleneck as the optimal length varies across states and tasks. In this paper, we propose Adaptive Action CHunking (ACH), a novel offline-to-online RL algorithm that dynamically modulates chunk length during both training and inference. To find the optimal chunk length for a dynamically varying current state, we simultaneously estimate action-values for all candidate chunk lengths in a single forward pass, using a Transformer-based architecture. Our mechanism allows the agent to select the most effective chunk length adaptively based on the current state. Evaluated on 34 challenging tasks, ACH consistently outperforms fixed-length baselines, demonstrating superior generalization and learning efficiency in complex environments.

Related papers

• ACSAC: Adaptive Chunk Size Actor-Critic with Causal Transformer Q-Network [19.806833366817383]
  Long-horizon, sparse-reward tasks pose a fundamental challenge for reinforcement learning.<n>Actor-critic methods with action chunking address this by operating over temporally extended actions.<n>We propose Adaptive Chunk Size Actor-Critic (ACSAC) to support flexible, state-dependent chunk sizes without task-specific tuning.
  arXiv  Detail & Related papers  (2026-05-10T10:00:34Z)
• Adaptive Action Chunking at Inference-time for Vision-Language-Action Models [22.3720651624276]
  Action chunking is a key technique to improve robotic manipulation abilities.<n>A large chunk size reduces the model's responsiveness to new information, while a small one increases the likelihood of mode-jumping.<n>We propose a novel Adaptive Action Chunking (AAC) strategy, which exploits action entropy as the cue to adaptively determine the chunk size.
  arXiv  Detail & Related papers  (2026-04-05T16:03:32Z)
• Decoupled Q-Chunking [63.864222078287575]
  We use chunked critics to estimate the value of short action sequences ("chunks") rather than individual actions, speeding up value backup.<n>Our key insight is to decouple the chunk length of the critic from that of the policy, allowing the policy to operate over shorter action chunks.<n>This design retains the benefits of multi-step value propagation while sidestepping both the open-loop sub-optimality and the difficulty of learning action chunking policies for long action chunks.
  arXiv  Detail & Related papers  (2025-12-11T18:52:51Z)
• Improving Generative Behavior Cloning via Self-Guidance and Adaptive Chunking [29.920087317401396]
  Generative Behavior Cloning is a simple yet effective framework for robot learning.<n>We propose two novel techniques to enhance the consistency and reactivity of diffusion policies.<n>Our approach substantially improves GBC performance across a wide range of simulated and real-world robotic manipulation tasks.
  arXiv  Detail & Related papers  (2025-10-14T11:16:34Z)
• FlowPG: Action-constrained Policy Gradient with Normalizing Flows [14.98383953401637]
  Action-constrained reinforcement learning (ACRL) is a popular approach for solving safety-critical resource-alential related decision making problems. A major challenge in ACRL is to ensure agent taking a valid action satisfying constraints in each step.
  arXiv  Detail & Related papers  (2024-02-07T11:11:46Z)
• Action-Quantized Offline Reinforcement Learning for Robotic Skill Learning [68.16998247593209]
  offline reinforcement learning (RL) paradigm provides recipe to convert static behavior datasets into policies that can perform better than the policy that collected the data. In this paper, we propose an adaptive scheme for action quantization. We show that several state-of-the-art offline RL methods such as IQL, CQL, and BRAC improve in performance on benchmarks when combined with our proposed discretization scheme.
  arXiv  Detail & Related papers  (2023-10-18T06:07:10Z)
• Stabilizing Q-learning with Linear Architectures for Provably Efficient Learning [53.17258888552998]
  This work proposes an exploration variant of the basic $Q$-learning protocol with linear function approximation. We show that the performance of the algorithm degrades very gracefully under a novel and more permissive notion of approximation error.
  arXiv  Detail & Related papers  (2022-06-01T23:26:51Z)
• Hyper-Learning for Gradient-Based Batch Size Adaptation [2.944323057176686]
  Scheduling the batch size to increase is an effective strategy to control noise when training deep neural networks. We introduce Arbiter as a new hyper-optimization algorithm to perform batch size adaptations for learnable schedulings. We demonstrate Arbiter's effectiveness in several illustrative experiments.
  arXiv  Detail & Related papers  (2022-05-17T11:01:14Z)
• Learning Salient Boundary Feature for Anchor-free Temporal Action Localization [81.55295042558409]
  Temporal action localization is an important yet challenging task in video understanding. We propose the first purely anchor-free temporal localization method. Our model includes (i) an end-to-end trainable basic predictor, (ii) a saliency-based refinement module, and (iii) several consistency constraints.
  arXiv  Detail & Related papers  (2021-03-24T12:28:32Z)
• Self Normalizing Flows [65.73510214694987]
  We propose a flexible framework for training normalizing flows by replacing expensive terms in the gradient by learned approximate inverses at each layer. This reduces the computational complexity of each layer's exact update from $mathcalO(D3)$ to $mathcalO(D2)$. We show experimentally that such models are remarkably stable and optimize to similar data likelihood values as their exact gradient counterparts.
  arXiv  Detail & Related papers  (2020-11-14T09:51:51Z)
• Discrete Action On-Policy Learning with Action-Value Critic [72.20609919995086]
  Reinforcement learning (RL) in discrete action space is ubiquitous in real-world applications, but its complexity grows exponentially with the action-space dimension. We construct a critic to estimate action-value functions, apply it on correlated actions, and combine these critic estimated action values to control the variance of gradient estimation. These efforts result in a new discrete action on-policy RL algorithm that empirically outperforms related on-policy algorithms relying on variance control techniques.
  arXiv  Detail & Related papers  (2020-02-10T04:23:09Z)

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.