RL-driven data mixing boosts evaluation scores
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An RL‑driven data scheduler can lift MMLU performance by 27.5 % relative while achieving a 2.23× higher HumanEval pass@1, and it does so with virtually no extra compute [1] . The scheduler learns a policy that decides, at each step, how many examples from each source task to present to the model. Because the policy operates online, the training loop sees only a 0.4 % wall‑clock increase per step. Before AC‑ODM, most LLM pre‑training pipelines relied on static or uniform mixing of source…
1Key Takeaways
- An RL‑driven data scheduler can lift MMLU performance by 27.5 % relative while achieving a 2.23× higher HumanEval pass@1, and it does so with virtually no extra compute [1] .
- The scheduler learns a policy that decides, at each step, how many examples from each source task to present to the model.
- Because the policy operates online, the training loop sees only a 0.4 % wall‑clock increase per step.
- Before AC‑ODM, most LLM pre‑training pipelines relied on static or uniform mixing of source….
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that an RL‑driven data scheduler can lift MMLU performance by 27.5 % relative while achieving a 2.23× higher HumanEval pass@1, and it does so with virtually no extra compute [1] .
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