Can 1000 carefully chosen examples align models effectively?
Does alignment require massive datasets, or can strategic curation of small, high-quality examples achieve comparable performance? LIMA tests whether quality beats quantity in post-training.
LIMA ("Less Is More for Alignment") establishes a foundational finding: given a strong pretrained language model, remarkably strong alignment performance can be achieved by fine-tuning on just 1,000 carefully curated training examples. This is the alignment-specific instantiation of a broader principle that pretraining does the heavy lifting and post-training is primarily about activating existing capabilities.
The finding connects to a converging evidence pattern across the vault:
- Can a single training example unlock mathematical reasoning? — one example activates reasoning in RLVR; 1000 activate alignment in SFT
- Can 78 demonstrations teach agency better than 10000? — 78 curated trajectories for agentic behavior; same principle
- Can models improve themselves on tasks without verifiable answers? — identical count (1000) for reasoning catalyst
The consistent pattern: post-training interventions require far less data than assumed, but the quality bar is high. Random data at scale underperforms curated data at small scale. This is the "Less Is More" principle — the pretrained model already contains the capabilities; post-training teaches it when and how to deploy them, not what they are.
For alignment specifically, the implication challenges the industry's data collection approach. Massive RLHF annotation efforts with thousands of labelers may be optimizing the wrong variable. Careful curation of a small number of high-quality examples, targeting the specific behavioral patterns desired, may achieve comparable results at a fraction of the cost.
Source: Alignment
Related concepts in this collection
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Can a single training example unlock mathematical reasoning?
Does minimal data suffice to activate latent reasoning capabilities in language models? This explores whether one example can produce dramatic performance gains comparable to much larger datasets.
extreme data efficiency for reasoning; LIMA is the alignment parallel
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Can 78 demonstrations teach agency better than 10000?
Does agentic capability depend on data volume or curation quality? LIMI achieves 73.5% on AgencyBench with 78 samples versus 24-45% for models trained on 10K+, suggesting strategic demonstration design may matter far more than scale.
curation > volume for agentic behavior
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Can models improve themselves on tasks without verifiable answers?
Most self-improvement methods require objective correctness signals, limiting them to math and code. Can models self-improve on open-ended instruction tasks where answers can't be automatically verified?
same count, same principle, different domain
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Do base models already contain hidden reasoning ability?
Explores whether reasoning capability emerges during pre-training as a latent feature rather than being created by post-training methods like reinforcement learning or fine-tuning.
the theoretical foundation: post-training activates, it doesn't create
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Can we train better models on less data?
Can gradient-based influence estimation identify which instruction data actually matters most? The research explores whether selecting small subsets of training data by their similarity to target capabilities might outperform training on everything.
LESS provides the principled mechanism for LIMA-style curation: gradient-based influence estimation can identify which alignment examples matter most, operationalizing "careful curation" as a computable selection criterion rather than manual judgment
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Original note title
1000 carefully curated alignment examples achieve remarkably strong performance — alignment is primarily about data quality not quantity