Unverified50% confidenceBenchmarkExact time
On MLE-Bench, RL does not yield task performance improvements over the untrained environment-modification approach.
1
Sources
50%
Confidence
Long-term
Relevance
10/9/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedSimply stacking the number of RL tasks does not lead to better training outcomes, and unsolvable tasks instead drag down overall training quality.72% similarUnverifiedSingle-pass training brings only modest overall calibration improvement and no improvement whatsoever in the early prefix stage.65% similarUnverifiedThe paper finds that the ranking of downstream tasks is not consistent with the ranking of training loss, and schemes with lower training loss do not necessarily perform better in downstream evaluations.65% similar
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