Applicability of DARS to open-ended tasks

Establish whether Dependency-Aware Reward Shaping can be applied effectively to open-ended tasks whose predicates lack clear logical dependencies and for which graph construction and dependency-based credit assignment are not straightforward.

Background

Dependency-Aware Reward Shaping represents task completion through predicates connected by prerequisite relations, making it most directly applicable to tasks with identifiable requirements and dependency structures. The evaluation covers household planning, web shopping, search, and mathematical reasoning, all of which permit domain-specific predicate graphs.

The paper notes that open-ended tasks may not provide clearly defined logical dependencies. Consequently, constructing a dependency graph and assigning credit through those dependencies may be difficult, and the effectiveness of DARS in that setting has not been established.

References

In open-ended tasks, predicates may lack clear logical dependencies, making graph construction and dependency-based credit assignment less straightforward. The applicability of DARS to such tasks remains to be established.

— Dependency-Aware Reward Shaping for Agentic Reinforcement Learning  (2610.01207 - Chen et al., 1 Oct 2026) in Section 6, Conclusion and Limitations