Unverified50% confidenceFactExact time
The core contradiction LM2LM faces is how to obtain scalable feedback without sacrificing reliability, because surrogate feedback becomes distorted as policy optimization shifts the proposal distribution.
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Long-term
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10/19/2026
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UnverifiedResearch reveals that LLMs do not reliably weight and update based on evidence reliability like an ideal Bayesian agent, but instead exhibit behavioral patterns close to distributional skew, with their influence being less correlated with the true reliability of evidence than the Bayesian framework would predict.73% similarUnverifiedLMArena's blind-test voting mechanism has been pointed out by researchers to have a prompt engineering bias, whereby models that are good at generating fluent, flattering responses score higher rather than those with genuinely stronger capabilities.72% similarUnverifiedSelf-generated feedback from the model may produce misleading pseudo-insights, and validator quality affects feedback effectiveness, which remains an open question for this method.72% similarUnverifiedThe prediction quality of the RWM depends heavily on the representativeness and number of in-context examples; when new proposals deviate from the distribution of in-context examples, prediction error rises significantly.70% similarUnverifiedThe low consistency between the feedback score and the held-out score is essentially a concrete manifestation of overfitting and the generalization gap in the scenario of agent self-improvement.70% similar
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