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A factorial experiment reveals that persona detail does not monotonically increase LLM opinion diversity; interaction architectures explore non-overlapping opinion regions; low-cost interventions like temperature scaling have negligible effects.
This paper introduces Experiential Learning (EL), a method that repurposes an LLM-as-a-Judge into an LLM-as-a-Coach to provide rich textual feedback instead of scalar rewards, improving performance and generalization on open-ended non-verifiable tasks.
VIBE is a framework that evaluates generative bias in Large Audio-Language Models using open-ended tasks with human-recorded speech, revealing systematic biases triggered by gender and accent cues.
This paper proposes a prompt-level reward specification framework that separates reward specification from computation, constructing reusable task-adaptive rubrics and executable constraint checkers offline to produce a hybrid reward for open-ended post-training without requiring human annotations or separate reward models.
SCOPE is a self-play framework for open-ended tasks that co-evolves a Challenger and Solver policy, achieving up to +10.4 points on benchmarks without external supervision.
ARES proposes a framework for automatically constructing rubric-based RL data from pretraining documents, generating question-answer pairs and weighted rubrics to enable instance-level reward supervision for open-ended LLM responses, outperforming existing methods on multi-dimensional open-ended tasks.
This paper introduces POW3R, a policy-aware rubric reward framework for reinforcement learning with verifiable rewards (RLVR). It shows that static rubric aggregation misallocates learning signal, and POW3R achieves faster convergence and better performance across multiple settings.
Cornell researchers propose POP, a self-play framework that lets an LLM generate its own rubrics and training pairs for open-ended tasks, boosting Qwen-2.5-7B on healthcare QA, creative writing and instruction following without human labels.