Unverified50% confidenceBenchmarkExact time
L2RR was empirically evaluated on six mathematical reasoning benchmarks and three model scales, and the results show that L2RR improves target-domain performance while also improving rollout efficiency.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
11/3/2026
First Seen
Valid until: 2/1/2027
Sources
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UnverifiedL2RR learns a discrete sampling distribution over source prompts by optimizing a bilevel objective: maximizing accuracy on the held-out target dataset while penalizing those rollouts that provide little incremental learning information.76% similarUnverifiedEvaluation on instruction-following, reasoning, and coding tasks shows that the flow models learned by Lafa are comparable to or even better than autoregressive models in quality.73% similarUnverifiedAutoregressive LLMs can already improve reasoning performance through chain-of-thought, RL fine-tuning, multiple sampling, and additional test-time compute.71% similarUnverifiedFor teams building large model inference services, a well-implemented and well-tuned LRU is often already a quite powerful choice, and alternative solutions should be comprehensively tested on actual workloads before a decision is made.70% similarUnverifiedExperiments found that fine-tuning can simultaneously improve both the optimization fidelity and the runtime speedup of the model.70% similar
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