aiqbench analysis
This research introduces a lightweight Transformer-enhanced critic for Soft Actor-Critic that leverages N-step returns to better capture temporal structure in long-horizon reinforcement learning tasks. By eliminating the need for importance sampling and target networks, the method achieves stable training with minimal architectural overhead while outperforming standard off-policy baselines. The approach demonstrates that sequence modeling directly within the critic is a viable path to solving sparse-reward problems without complex actor-side modifications.
Chunking the Critic: A Transformer-based Soft Actor-Critic with N-Step Returns
arXiv:2503.03660v4 Announce Type: replace Abstract: We introduce a sequence-conditioned critic for Soft Actor-Critic (SAC) that models trajectory context with a lightweight Transformer and trains on aggregated $N$-step targets. Unlike prior approaches that (i) score state-action pairs in isolation or (ii) rely on actor-side action chunking to handle long horizons, our method strengthens the critic itself by conditioning on short trajectory segments and integrating multi-step returns -- without importance sampling (IS). The resulting sequence-aware value estimates capture the critical temporal structure for extended-horizon and sparse-reward problems. On local-motion benchmarks, we further show that freezing critic parameters for several steps makes our update compatible with CrossQ's core idea, enabling stable training \emph{without} a target network. Despite its simplicity -- a 2-layer Transformer with 128-256 hidden units and a maximum update-to-data ratio (UTD) of $1$ -- the approach consistently outperforms standard SAC and strong off-policy baselines, with particularly large gains on long-trajectory control. These results highlight the value of sequence modeling and $N$-step bootstrapping on the critic side for long-horizon reinforcement learning.
Abstract:We introduce a sequence-conditioned critic for Soft Actor-Critic (SAC) that models trajectory context with a lightweight Transformer and trains on aggregated $N$-step targets. Unlike prior approaches that (i) score state-action pairs in isolation or (ii) rely on actor-side action chunking to handle long horizons, our method strengthens the critic itself by conditioning on short trajectory segments and integrating multi-step returns -- without importance sampling (IS). The resulting sequence-aware value estimates capture the critical temporal structure for extended-horizon and sparse-reward problems. On local-motion benchmarks, we further show that freezing critic parameters for several steps makes our update compatible with CrossQ's core idea, enabling stable training \emph{without} a target network. Despite its simplicity -- a 2-layer Transformer with 128-256 hidden units and a maximum update-to-data ratio (UTD) of $1$ -- the approach consistently outperforms standard SAC and strong off-policy baselines, with particularly large gains on long-trajectory control. These results highlight the value of sequence modeling and $N$-step bootstrapping on the critic side for long-horizon reinforcement learning.
Machine Learning (cs.LG)
arXiv:2503.03660 [cs.LG]
(or arXiv:2503.03660v4 [cs.LG] for this version)
Full text under the publisher's open licence · 175 words
Related coverage
[AINews] High Taste Testers Ep. 1
Latent Space · Oct 17, 2026
Problems of Proliferation
LessWrong · Sep 29, 2026
Distributed Dexterous Manipulation with Spatially Conditioned Multi-Agent Transformers
arXiv · Sep 9, 2026
TempoNet: Slack-Quantized Transformer-Guided Reinforcement Scheduler for Adaptive Deadline-Centric Real-Time Dispatchs
arXiv · Jul 23, 2026