  Despite the promise of RLHF in aligning LLMs with human preferences, it often
leads to superficial alignment, prioritizing stylistic changes over improving
downstream performance of LLMs. Underspecified preferences could obscure
directions to align the models. Lacking exploration restricts identification of
desirable outputs to improve the models. To overcome these challenges, we
propose a novel framework: Reinforcement Learning from Reflective Feedback
(RLRF), which leverages fine-grained feedback based on detailed criteria to
improve the core capabilities of LLMs. RLRF employs a self-reflection mechanism
to systematically explore and refine LLM responses, then fine-tuning the models
via a RL algorithm along with promising responses. Our experiments across
Just-Eval, Factuality, and Mathematical Reasoning demonstrate the efficacy and
transformative potential of RLRF beyond superficial surface-level adjustment.
