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Group-relative reinforcement learning (RL) relies on reward variation among sampled responses to estimate informative relative advantages. As language models become increasingly capable, existing training data can become reward-saturated: all sampled responses to the same problem might receive equally high rewards, whe...
An extensive evaluation of automatic harness evolution for LLM agents is conducted, comparing harness evolution with simple test-time scaling and discovery baselines under comparable feedback and inference budgets, and also evaluating evolved harnesses on held-out tasks to assess whether the discovered improvements gen...
FLIP (FLipped Inference for Prompt reconstruction), a reference-free and rubric-free reward modeling approach that reformulates reward modeling through backward inference that enables reliable reward modeling in downscaled regimes where judgment methods fail, is proposed.
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