Reducing Credit Assignment Variance via Counterfactual Reasoning Paths

In three linesResearchers introduce IBPO (Implicit Behavior Policy Optimization), a credit assignment method for reinforcement learning with LLMs. By comparing multiple reasoning trajectories, the framework transforms sparse terminal rewards into step-sensitive learning signals, reducing gradient variance and improving stability on mathematical and code reasoning benchmarks.

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