Driving · 2026-10-06
Hierarchical Reinforcement Learning with Stable Temporal Abstraction for Language Model Agents
Shayan Mohajer Hamidi, Yize Cheng, Yuanda Xu, Zhengze Zhou, Alborz Geramifard
AI-assisted summary based on the paper. Consult the linked source for methods and limitations. Benchmark numbers appear after editor verification.
TL;DR
The authors study “temporal abstraction instability” in hierarchical language-model agents: the boundary policy can switch subgoals too often (premature replanning) or keep them too long (stale persistence). They propose STAC (Stable Temporal Abstraction via Constrained Optimization), which adds duration-dependent costs only to the sampled boundary decision, leaving other reward/advantage/critic signals unchanged. STAC improves success over a hierarchical baseline on ALFWorld and WebShop.
Why it matters
Explicit subgoal syntax and multi-timescale credit assignment do not guarantee that learned temporal abstraction is stable. STAC targets the specific failure mode at the boundary policy level—premature replanning and stale persistence—using constrained optimization with decision-local costs, aiming for multi-turn valid subgoals and better multi-phase success.
Method
- Adopt the HiPER Plan–Execute host unchanged, including its boundary/subgoal/action advantage streams and critic targets; STAC reads only the host’s normalized boundary advantage.
- Define short and stale bounded cost functions based on subgoal duration and apply a Lagrangian relaxation by penalizing only the sampled boundary decision span (KEEP vs SWITCH).
- Use fixed-multiplier STAC (μshort=μstale=0.1) with prespecified duration heuristics dmin=2 and dmax=8; costs do not alter environment rewards, critic targets, subgoal advantages, or primitive-action advantages.
Limitation
The paper notes the reported duration values are pre-specified heuristics (dmin=2, dmax=8), and the experiments hold temporal-abstraction multipliers fixed rather than using adaptive dual optimization under user-specified budgets; they also do not establish whether other separately tuned host boundary controls could recover the same gains.
Abstract (from arXiv)
Hierarchical reinforcement learning improves long-horizon control by organizing primitive actions around persistent subgoals and assigning credit at multiple temporal scales. Recent hierarchical language agents bring these benefits to interactive tasks by explicitly separating subgoal planning from action execution. We observe, however, that an explicit hierarchy does not by itself determine how stable the resulting temporal abstraction is: the learned boundary policy may replace the subgoal almost every turn, making it effectively transient, or retain a subgoal after it has stopped being appropriate. We call this temporal abstraction instability. We propose Stable Temporal Abstraction via Constrained Optimization (STAC), a constrained boundary-policy optimization method that represents premature replanning and stale persistence as constraint costs. STAC applies the resulting Lagrangian costs only to the sampled boundary decision, leaving the underlying algorithm's rewards, critic targets, subgoal advantages, and primitive-action advantages unchanged. Across two backbones and two benchmarks, STAC improves success over a strong hierarchical baseline by $8.1$ and $7.9$ points on ALFWorld and WebShop with Qwen3-0.6B, and by $23.5$ and $15.8$ points with Llama-3.2-1B-Instruct.
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Publication record
Bibliographic evidence describes where and when a record appeared. It does not validate scientific claims.
First public release: 2026-10-04 · Indexed by Pub-AI: 2026-10-06 UTC
Hierarchical Reinforcement Learning with Stable Temporal Abstraction for Language Model Agents
Preprint posted: 2026-10-04 · Original record ↗
- arxiv
- 2610.05473
Explicit source versions (1)
- v1 · 2026-10-04
- Preprint available
Source date: 2026-10-04 · First detected: 2026-10-25 UTC · Historical baseline
Source evidence (1)
- Official evidence ↗ · atom:published (official metadata)
Abstract · arxiv
Hierarchical reinforcement learning improves long-horizon control by organizing primitive actions around persistent subgoals and assigning credit at multiple temporal scales. Recent hierarchical language agents bring these benefits to interactive tasks by explicitly separating subgoal planning from action execution. We observe, however, that an explicit hierarchy does not by itself determine how stable the resulting temporal abstraction is: the learned boundary policy may replace the subgoal almost every turn, making it effectively transient, or retain a subgoal after it has stopped being appropriate. We call this temporal abstraction instability. We propose Stable Temporal Abstraction via Constrained Optimization (STAC), a constrained boundary-policy optimization method that represents premature replanning and stale persistence as constraint costs. STAC applies the resulting Lagrangian costs only to the sampled boundary decision, leaving the underlying algorithm's rewards, critic targets, subgoal advantages, and primitive-action advantages unchanged. Across two backbones and two benchmarks, STAC improves success over a strong hierarchical baseline by $8.1$ and $7.9$ points on ALFWorld and WebShop with Qwen3-0.6B, and by $23.5$ and $15.8$ points with Llama-3.2-1B-Instruct.