新闻
Scaling Near-Optimal SFT-RL Annotation Budget Allocation from Small to Large LLMs
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Research shows how to split annotation budgets between supervised fine-tuning and reinforcement learning for LLM training, with findings that transfer from small to large models.
- 为何重要
- Matters for ML engineers optimizing training costs and annotation allocation when post-training language models with fixed labeling budgets.
- 注意
- The near-optimal region widens with model scale, so ratios found on small models may not pinpoint exact optima for larger models, only viable ranges.
- llm
- fine-tun
- post-train
- reinforcement learning
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