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ProCredit: From Outcome Rewards to Progress Credit in Agentic Reinforcement Learning
Long-horizon agentic tasks require an agent to modify an environment through a sequence of tool calls, with success determined by the final state. The standard recipe assigns a single outcome reward at the end and compares trajectories sampled for the same task. As a result, a group with no successf
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