Critical-State RL Improves Multi-Turn Tool Use in Salesforce Paper
The insight is that most reward variation in multi-turn RL comes from downstream noise, so credit assignment should target the pivotal call rather than the full trajectory. Researchers and engineers building tool-using agents can apply nested sampling and contextual-bandit updates to concentrate training on decision-critical states. The BFCL v4 result offers a concrete benchmark for comparing against trajectory-level reward approaches.
