Actor, Judge, Teacher: How Long‑Horizon Agents Make Decisions
agents reinforcement-learning
| Source: HF Papers | Original article
Decision models can serve as actors, judges or teachers for long‑horizon agents, selecting actions, evaluating past moves, or scoring actions to train policies via reinforcement learning.
A new study released on 12 November 2026 provides the first systematic comparison of “decision models” when they are cast as Actors, Judges or Teachers for long‑horizon tasks. The researchers evaluated three purpose‑built decision models alongside three general‑purpose large language models on two benchmark environments – ALFWorld, a simulated indoor navigation suite, and WebShop, an online shopping scenario.
When used as Actors, all six models displayed a consistent bias toward completing an easier subgoal before tackling the primary objective, and a generic reminder in the prompt failed to curb this behaviour. By contrast, the same models could serve as Judges that evaluate entire action trajectories after the fact, and as Teachers that feed those evaluations back into reinforcement‑learning pipelines. Remarkably, a model that performed poorly as an Actor still acted as an effective Teacher, improving the success rate of learned policies on both benchmarks.
The findings matter because they decouple the ability to act from the ability to teach. In practice, a weak decision‑making component could still be valuable for supervising or shaping other agents, opening a path to more modular AI systems where separate models specialise in execution, assessment and curriculum generation. The work also highlights a limitation of prompt engineering: simple reminders do not reliably steer long‑horizon agents away from suboptimal shortcuts.
Going forward, researchers will likely probe how to strengthen the Actor role—perhaps by designing better subgoal‑aware prompts or training regimes—while exploiting the teaching potential of even modest decision models. Monitoring follow‑up studies that scale these roles to more complex domains such as robotics or multimodal interaction will be key, especially in light of recent moves to apply large models directly to robotic control.
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