From Outcomes to Steps: Reliable Success-Aware Critic Learning for Long-Horizon Language Agents
Reinforcement learning for long-horizon language agents requires identifying whether intermediate actions make eventual task success more or less likely when supervision is available only at trajectory level. The main difficulty is that a critic with aggregate return-prediction accuracy may still fail to resolve the local value differences needed to assign credit to actions, especially under sparse feedback and function approximation. Conventional critics estimate future return, but policy improvement also depends on knowing whether each intermediate decision makes task success more or less likely. We introduce SCALE, an actor-critic framework that augments conventional return estimation ...
| Authors | Jiaxi Liu, Shixiong Kai, Zhentao Tang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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