Entity · paper

Verifiable Self-Evolution for Open-Ended Dialogue Skills via Future-Feedback Prediction

paperactiveprovisionalverifiable-self-evolution-for-open-ended-dialogue-skills-via-future-feedback-prediction-002dcac4·1 events·first seen 4d ago

Aliases: Verifiable Self-Evolution for Open-Ended Dialogue Skills via Future-Feedback Prediction

More like this (12)

Introspective Coupling: Self-Explanation Training Tracks Behavioral Change Despite Fixed Supervision Learning from the Self-future: On-policy Self-distillation for dLLMs Schema-Guided Dialogue Personalized Evaluation as Learning Reinforcement Learning with Metacognitive Feedback Visually Grounded Self-Reflection for Vision-Language Models via Reinforcement Learning EvolveNav: Proactive Preflection and Self-Evolving Memory for Zero-Shot Object Goal Navigation Accuracy and Satisfaction in Multi-Turn LLM Dialogues for NFR Assessment Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMs Measuring Semantic Progress in Multi-turn Dialogue via Information Gain A Self-Evolving Agent for Longitudinal Personal Health Management Multi-Turn Evaluation of Deep Research Agents Under Process-Level Feedback

Recent events (1)

5arXiv · cs.CL·4d ago·source ↗

Future-Feedback Prediction enables verifiable self-evolution of dialogue skills in frozen LLM agents

Researchers propose a method called future-feedback skill evolution that reframes open-ended dialogue skill improvement as a prediction task: rather than evaluating counterfactual responses directly, the system predicts whether an observed response will lead to positive or negative subsequent user signals, making the target verifiable on fixed logged data. The approach achieves over 75% prediction accuracy on a proprietary sales-assistant dataset and enables offline textual skill optimization without requiring live traffic exposure. The core contribution is converting the inherently moving target of conversational feedback into a stable offline learning signal, addressing a key gap in applying self-evolution techniques beyond math and code domains.

Agent and Tool Ecosystem Alignment and RLHF Verifiable Self-Evolution for Open-Ended Dialogue Skills via Future-Feedback Prediction