Reinforcement Learning from AI Feedback (RLAIF) has demonstrated significant
potential across various domains, including mitigating harm in LLM outputs,
enhancing text summarization, and mathematical reasoning. This paper introduces
an RLAIF framework for improving the code generation abilities of lightweight
(<1B parameters) LLMs. We specifically focus on code generation tasks that
require writing appropriate API calls, which is challenging due to the
well-known issue of hallucination in LLMs. Our framework extracts AI feedback
from a larger LLM (e.g., GPT-3.5) through a specialized prompting strategy and
uses this data to train a reward model towards better alignment from smaller
LLMs. We run our experiments on the Gorilla dataset and meticulously assess the
quality of the model-generated code across various metrics, including AST,
ROUGE, and Code-BLEU, and develop a pipeline to compute its executability rate
accurately. Our approach significantly enhances the fine-tuned LLM baseline's
performance, achieving a 4.5% improvement in executability rate. Notably, a
smaller LLM model (780M parameters) trained with RLAIF surpasses a much larger
fine-tuned baseline with 7B parameters, achieving a 1.0% higher code
executability rate.

使用 AI 反馈的强化学习（RLAIF）已在多个领域展示了巨大的潜力，包括减少 LLM 输出中的伤害、提升文本摘要以及数学推理等。本文引入了一个 RLAIF 框架，用于提高轻量级（小于 1B 参数）LLMs 的代码生成能力，特别关注需要编写适当 API 调用的代码生成任务，并通过专门的提示策略从更大的 LLM（例如 GPT-3.5）中提取 AI 反馈数据，用于训练更小 LLMs 的奖励模型以实现更好的对齐。我们在 Gorilla 数据集上运行实验，并通过 AST、ROUGE 和 Code-BLEU 等多个指标精确评估模型生成的代码的质量，并开发一个能够准确计算其可执行性率的流程。我们的方法显著提升了微调 LLM 基线的性能，使可执行性率提高了 4.5%。值得注意的是，使用 RLAIF 训练的一个更小的 LLM 模型（780M 参数）超过了一个具有 7B 参数的更大的微调基线，使得代码的可执行性率提高了 1.0%。

应用 RLAIF 用于轻量级 LLMs 中的 API 使用的代码生成

Applying RLAIF for Code Generation with API-usage in Lightweight LLMs

Recently developed large language models (LLMs) such as ChatGPT, Claude, and
Llama have demonstrated impressive abilities, and even surpass human-level
performance in several tasks. Despite their success, the resource-intensive
demands of these models, requiring significant computational power for both
training and inference, limit their deployment to high-performance servers.
Additionally, the extensive calculation requirements of the models often lead
to increased latency in response times. With the increasing need for LLMs to
operate efficiently on CPUs, research about lightweight models that are
optimized for CPU inference has emerged. In this work, we introduce GEB-1.3B, a
lightweight LLM trained on 550 billion tokens in both Chinese and English
languages. We employ novel training techniques, including ROPE,
Group-Query-Attention, and FlashAttention-2, to accelerate training while
maintaining model performance. Additionally, we fine-tune the model using 10
million samples of instruction data to enhance alignment. GEB-1.3B exhibits
outstanding performance on general benchmarks such as MMLU, C-Eval, and CMMLU,
outperforming comparative models such as MindLLM-1.3B and TinyLLaMA-1.1B.
Notably, the FP32 version of GEB-1.3B achieves commendable inference times on
CPUs, with ongoing efforts to further enhance speed through advanced
quantization techniques. The release of GEB-1.3B as an open-source model marks
a significant contribution to the development of lightweight LLMs, promising to
foster further research and innovation in the field.

最近发展的大型语言模型（LLMs）（如 ChatGPT、Claude 和 Llama）展示了惊人的能力，甚至在多项任务中超越了人类水平。然而，这些模型对资源的需求在训练和推断方面都需要大量的计算能力，限制了它们应用于高性能服务器。鉴于在 CPU 上高效运行 LLMs 的需求日益增长，我们介绍了 GEB-1.3B，一个在中文和英文语言中训练了 5500 亿标记的轻量级 LLM。我们采用了一些新的训练技术，包括 ROPE、Group-Query-Attention 和 FlashAttention-2，以加速训练同时保持模型的性能。此外，我们使用了 1000 万条指示数据样本对模型进行了微调以提高对齐度。GEB-1.3B 在 MMLU、C-Eval 和 CMMLU 等常规基准测试中表现出色，优于 MindLLM-1.3B 和 TinyLLaMA-1.1B 等对比模型。值得注意的是，GEB-1.3B 的 FP32 版本在 CPU 上具有可嘉的推断时间，正在进行先进的量化技术来进一步提高速度。GEB-1.3B 作为一个开源模型的发布对于轻量级 LLMs 的发展具有重要意义，有望促进该领域的进一步研究和创新。