Reinforcement learning (RL) is a standard approach for training language agents in interactive environments, but it depends on reward signals. When rewards are unavailable, RL cannot be applied directly. A new arXiv preprint (2610.11384) addresses this limitation by focusing on how environmental feedback is handled.
The abstract notes that recent methods use environmental feedback as privileged context, but argues that the way this feedback is modeled matters. The paper's title points to a rethinking of feedback treatment within agentic hindsight self-distillation, suggesting that subtle choices in feedback modeling may have significant effects on agent performance.
Because the abstract is truncated, the specific method and experimental results are not available from the source. The contribution appears to be a conceptual rethinking rather than a purely algorithmic tweak, which may be useful for researchers working on reward-free agent training.