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TideRL boosts RL training goodput by up to 5.6 times, transforming how we approach efficiency in multi-turn agentic workloads.
Single-rollout sampling can dramatically enhance the stability and effectiveness of RL in large language models, outperforming traditional methods in challenging benchmarks.