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Agentic reinforcement learning (RL) has emerged as a powerful approach for training large language model agents on multi-step tasks, yet reliance on terminal outcome rewards creates two credit-assignment problems, particularly in long-horizon tasks. First, same-outcome rollout groups provide no learning signal from ter...
Three findings emerge: joint depth-complexity coverage outperforms single-axis recipes; reasoning families respond non-uniformly, with abductive reasoning degrading outside the RL-covered region and task correlations clustering into deductive-abductive and inductive-analogy pairs; and uniform mixing outperforms staged...
A method that represents language models by log-likelihood vectors over prompt-response pairs and constructs model maps for comparing their conditional distributions is proposed, which supports the analysis and prediction of input-dependent model behavior.
A method for determining domain weights by viewing models as points in log-likelihood space and aligning the training update direction with the direction toward the target model is proposed.
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