Not from the final score alone. Imagine an agent spends ten steps searching, calls two tools, and produces a correct answer. It gets a positive terminal reward. A policy-gradient update can raise the probability of the sampled policy actions in that successful trajectory, adjusted by an advantage estimate. It does not automatically know that search step three found the decisive fact while tool call seven was wasted. OpenAI's policy-optimization introduction explains how future rewards weight earlier actions, and its PPO documentation explains advantage-based updates. The exact credit depends on the rollout and estimator used.

There are two different failure modes here. If the trainer accidentally includes tool-output tokens in the policy loss, it may credit the model for text it did not choose. The tool returned JSON. Why is the RL policy getting credit for those tokens? covers that. Even with the mask correct, sparse end-of-run reward has weak information about which policy decisions caused success. A value baseline can lower variance, but it is a prediction of expected return, not a proof that a particular tool call was causal. Long trajectories and environment noise make that distinction harder.

I would log the decision trace with action IDs and environment observations, then compare successful and unsuccessful runs with similar starting tasks. Are there early decisions that consistently separate outcomes? Can we construct safe counterfactual replays, for example changing one tool choice while holding the external fixture fixed? If we add intermediate rewards for valid citations, budget discipline or task progress, check that those signals actually align with final user success and cannot be gamed by repeatedly taking easy steps.

The interviewer may ask whether a high final win rate is enough. It may be enough to ship a measured policy if cost and safety also pass. It does not prove the learned strategy is robust. Test a shifted tool environment, fewer available calls and cases where the once-useful early search now returns misleading data. A Principal-level training design needs an explicit reward and credit contract, plus evaluation that distinguishes causal progress from a lucky successful trajectory.