Learning Multiple Timescales for Goal-Conditioned Reinforcement Learning
Offline goal-conditioned RL (GCRL) struggles with long-horizon tasks because discounting shrinks value differences between distant states until they fall below function approximation error. This work introduces GITA, which learns a single value network conditioned on a temporal abstraction stride $k$ and trains one high-level policy by aggregating advantage-weighted regression updates across multiple scales. By implicitly letting the best-suited scale dominate per state-goal pair, GITA raises average success rate on OGBench by 25 percentage points over HIQL without requiring a problem-specific fixed $k$.
GITA is a well-motivated and empirically solid contribution to offline hierarchical GCRL. The central claim—that no fixed temporal abstraction scale can simultaneously resolve nearby states and propagate signal to distant goals—is supported by analytical bounds, ablation studies, and strong aggregate results on locomotion tasks. However, the gains are not universal: the method underperforms simpler non-hierarchical alternatives on state-based manipulation tasks, and the theoretical analysis relies on an idealized shortest-path model that does not incorporate function approximation or dataset inadequacies.
The analytical core of the paper is clean and informative. Proposition 1 decomposes the high-level advantage into a reachability factor that grows with $k$ and a resolution factor that shrinks with $k$, making the distance-dependent trade-off explicit. This is not mere intuition dressed as theory; the subsequent ablation in Table 2 shows that conditioning the value function on $k$ is critical—an unconditioned network exposed to the same multi-scale data fails dramatically.
First, the theoretical model assumes an idealized shortest-path MDP and a continuous relaxation of the abstract horizon; the authors explicitly warn that it is not intended to characterize dataset-induced transitions, leaving a gap between the formal trade-off and the empirical reality of offline RL with function approximation. Second, despite the aggregate headline improvement, GITA is not dominant everywhere: on HumanoidMaze navigate large it trails OTA (73% vs 82%), and it is consistently outclassed by GCIQL on state-based Cube and Scene, where the dataset structure (non-Markovian, open-loop play data) appears to break hierarchical methods generally. Third, the high-level policy relies on a hand-designed Fibonacci candidate set $\mathcal{K}=\{3,5,8,13,21\}$, and the ablation shows that replacing it with geometrically sampled factors causes collapse on the widest-distance regimes, hinting at sensitivity to the spacing and range of scales.
The evidence broadly supports the main claims, but context matters. The progression from HIQL (no value abstraction) to OTA (fixed $k$) to GITA (multi-scale) is clearly traced, and GITA's aggregate wins are statistically significant under a Holm-Bonferroni-corrected Wilcoxon test across 23 tasks. The comparison with OTA is especially fair because OTA tunes its abstraction factor per environment, whereas GITA deploys a single $\mathcal{K}$ universally. Still, the reported relative gains are driven largely by locomotion and pixel-based tasks; the state-based manipulation results weaken the universal framing.
Reproducibility prospects are strong. The authors release code and build directly on the established OGBench/HIQL codebase, minimizing reimplementation variance. Hyperparameters are listed exhaustively in Table 5, including the architecture, FiLM conditioning, subgoal offsets, and goal-sampling probabilities. One caution is that the discount factor $\gamma$ is re-tuned per task and also determines the geometric sampling parameter $\alpha$, so independent reproduction requires replicating the grid search over $\{0.98,0.99,0.995\}$ per environment.
Existing approaches to offline goal-conditioned reinforcement learning (GCRL) struggle with long-horizon tasks. Discounting shrinks value differences between distant states until they fall below the function approximation error, leaving the agent with no signal for ranking states. Temporal abstraction, which treats k environment steps as a single transition, restores this signal at long range, but no single fixed k suits all state-goal distances: large k preserves value differences across long temporal distances while collapsing distinctions between nearby states, and small k does the reverse. We make this trade-off explicit and introduce Generalized Implicit Temporal Abstraction (GITA), which conditions a single value function on k. GITA trains one policy by aggregating advantage-weighted supervision across multiple k values, so scales assigning larger positive advantages to a state-goal pair contribute more strongly to its update. GITA does not need to choose between local resolution and long-range signal; it retains both without committing to a single k. On OGBench, GITA outperforms a broad range of offline GCRL baselines, raising average success rate across all tasks by 25 percentage points (73% relative improvement) over HIQL. It also improves over the strongest fixed-k method, OTA, by 7 percentage points (14% relative).
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