Salesforce RIVER filters defective RL environments for terminal agents
Environment quality, not environment quantity, may be the real bottleneck in agentic RL — a collection that looks large can be mostly reward noise. Teams training agents should audit verifiers before scaling data, and can adopt RIVER's filtering plus repetition penalties. River-8B's open checkpoints offer a strong 8B terminal-agent baseline against other open RL-trained models of the same size.
