Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as an effective approach for enhancing the reasoning capabilities of Large Language Models (LLMs). Despite its efficacy, RLVR faces a meta-learning bottleneck: it lacks mechanisms for error attribution and experience internalization intrinsic to the human learning cycle beyond practice and verification, thereby limiting fine-grained credit assignment and reusable knowledge formation. We term such reusable knowledge representations derived from past errors as meta-experience. Based on this insight, we propose **M**eta-**E**xperience **L**earning (**MEL**), a novel framework that incorporates self-distilled meta-experience into the model’s parametric memory. Building upon standard RLVR, we introduce an additional design that leverages the LLM’s self-verification capability to conduct contrastive analysis on paired correct and incorrect trajectories, identify the precise bifurcation points where reasoning errors arise, and summarize them into generalizable meta-experience. The meta-experience is further internalized into the LLM’s parametric memory by minimizing the negative log-likelihood, which induces a language-modeled reward signal that bridges correct and incorrect reasoning trajectories and facilitates effective knowledge reuse. Experimental results demonstrate that MEL achieves consistent improvements on benchmarks, yielding 3.92%–4.73% Pass@1 gains across varying model sizes.

This paper describes our system submitted on the third automatic simultaneous translation workshop at NAACL2022. We participate in the Chinese audio->English text direction of Chinese-to-English translation. Our speech-to-text system is a pipeline system, in which we resort to rhymological features for audio split, ASRT model for speech recoginition, STACL model for streaming text translation. To translate streaming text, we use wait-k policy trained to generate the target sentence concurrently with the source sentence, but always k words behind. We propose a competitive simultaneous translation system and rank 3rd in the audio input track. The code will release soon.