One of the most widely used approaches in continual learning is referred to
as replay. Replay methods support interleaved learning by storing past
experiences in a replay buffer. Although there are methods for selectively
constructing the buffer and reprocessing its contents, there is limited
exploration of the problem of selectively retrieving samples from the buffer.
Current solutions have been tested in limited settings and, more importantly,
in isolation. Existing work has also not explored the impact of duplicate
replays on performance. In this work, we propose a framework for evaluating
selective retrieval strategies, categorized by simple, independent class- and
sample-selective primitives. We evaluated several combinations of existing
strategies for selective retrieval and present their performances. Furthermore,
we propose a set of strategies to prevent duplicate replays and explore whether
new samples with low loss values can be learned without replay. In an effort to
match our problem setting to a realistic continual learning pipeline, we
restrict our experiments to a setting involving a large, pre-trained, open
vocabulary object detection model, which is fully fine-tuned on a sequence of
15 datasets.

通过研究回放方法在连续学习中的应用，本文提出了一种评估选择性检索策略的框架，并评估了多种策略的表现，并提出了防止重放重复的策略，探索是否可以通过学习低损失值的新样本来避免回放。

谨慎行事：规模化持续学习的最佳检索

Watch Your Step: Optimal Retrieval for Continual Learning at Scale

Recent advances in retrieval-augmented generation (RAG) have initiated a new
era in repository-level code completion. However, the invariable use of
retrieval in existing methods exposes issues in both efficiency and robustness,
with a large proportion of the retrieved contexts proving unhelpful or harmful
to code language models (code LMs). To tackle the challenges, this paper
proposes a selective RAG framework where retrieval is avoided when unnecessary.
To power this framework, we design a self-supervised learning approach that
enables a code LM to accurately self-evaluate whether retrieval can improve its
output quality and robustly leverage the potentially noisy retrieved contexts.
Using this LM as both the selective retrieval policy and the generation model,
our framework consistently outperforms the state-of-the-art prompting with an
invariable retrieval approach on diverse benchmarks including RepoEval,
CrossCodeEval, and a new benchmark. Meanwhile, our selective retrieval strategy
results in strong efficiency improvements by as much as 70% inference speedup
without harming the performance. We demonstrate that our framework effectively
accommodates different generation models, retrievers, and programming
languages. These advancements position our framework as an important step
towards more accurate and efficient repository-level code completion.

通过选择性检索和自我评估，本文提出了一种选择性检索和生成框架，以在大型代码库级别的代码自动补全中提高生成模型的准确性和效率。