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6 results for “MMLU-Pro”

1. Research · Evaluation & benchmarks

   Scaling curves can be certified with far fewer sampled answers

   Cheap to Draw, Expensive to Trust: Certifying Test-Time Scaling Curves

   A variance-aware audit provides simultaneous confidence bands across test-time budgets while reducing the number of generated answers.

   MMLU-Pro: 192000 generated answers (as reported)

   7 Oct 2026PMCC AI Lab, Peter Munk Cardiac Centre, University Health Network

2. Research · Reinforcement learning

   Turning failed reasoning attempts into training signal improves GRPO on hard problems

   Failure Is the Mother of Success: Recovering Supervision with Grounding Hindsight

   RSHD generates corrective notes from failures, verifies them via replays, and distills successful behaviors into the policy without extra inference cost.

   MMLU-Pro: 60.7% accuracy (as reported)

   16 Oct 2026University of California, San Diego, Adobe Research, University of Virginia

3. Research · Language models

   Query-adaptive neuron steering improves frozen language models across benchmarks

   Query-Adaptive Neuron Steering for LLMs

   Q-NeuS uses each query’s internal activation pattern to select and scale neuron interventions without updating the underlying model.

   MMLU-Pro: 59.11% accuracy (as reported)

   15 Oct 2026Northeastern University, Washington University in St. Louis, Nanjing University of Aeronautics and Astronautics

4. Research · Evaluation & benchmarks

   Language models’ own confidence becomes harder to outperform as they improve

   Predictable performance is suboptimal, and what it means for model routing

   Zhang and Hardt connect prediction gains beyond a model’s own confidence to its room for improvement, then test the relationship across 16 language models.

   MMLU-Pro: 0.65 Pearson correlation between empirical suboptimality and (as reported)

   23 Oct 2026

5. Research · Language models

   Alternating context edits and logit steering boosts frozen LLM accuracy

   EMO: A Closed-Loop Test-Time Computation Framework via Alternating Context Optimization and Logits Steering

   EMO closes the loop between feedback-driven context revision and decoding-time distribution correction, improving zero-shot performance across eight models and six benchmarks.

   MMLU-Pro: 3.76 pts accuracy improvement over TextGrad (as reported)

   16 Oct 2026Peking University, Samsung Research

6. Research · Reinforcement learning

   Posterior rewards become indistinguishable for long reasoning traces in LLM training

   Rethinking Probability-Based Reinforcement Learning From Posterior Concentration

   The authors identify a length-dependent failure mode they call Posterior Concentration Phenomenon, and propose a framework to mitigate it.

   MMLU-Pro: 61.3% Avg@2 (as reported)

   7 Oct 2026National University of Singapore, The Hong Kong University of Science and Technology, University of Science and Technology of China