Ubiquant’s One-Shot Entropy Minimization Shows Major Promise
Created on June 4|Last edited on June 4
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A new method out of Ubiquant’s AI research division is challenging one of the core assumptions of modern large language model training: that smarter models require more data and elaborate supervision. Dubbed One-shot Entropy Minimization, or EM, the technique forgoes labeled data entirely and uses a single, strategically chosen prompt to measurably improve a model’s reasoning in just a few training steps. This represents a potentially major simplification of current post-training methods and could influence how the AI community approaches fine-tuning.
A Minimalist Alternative to RLHF
Most reasoning-focused LLM enhancements today rely on reinforcement learning from human feedback, or RLHF. That method, used by companies like OpenAI and Anthropic, requires large-scale human annotation to define what “better” means, followed by iterative training with reward models. EM bypasses all of that. There are no human labels, no separate reward networks. Instead, EM focuses on the model’s own confidence, measuring how uncertain it is when responding to a single complex prompt and then tuning the model to reduce that internal entropy. In other words, it nudges the model to believe more strongly in what it already tends to think is the correct answer.
Entropy Minimization and Self-Confidence
The core idea behind EM is straightforward but novel. Large models often assign some probability to the correct answer but hesitate due to distribution over competing responses. EM identifies this spread in probabilities—the entropy—and fine-tunes the model to compress it, effectively increasing the confidence in its top-ranked output. This mirrors how humans sometimes “know” the answer but lack conviction. By applying this training signal on a single task that the model sometimes gets wrong and sometimes gets right, EM teaches the model to lean into its latent knowledge.
Selecting the Right Prompt
Not every problem works. The chosen prompt must be difficult enough that the model is unsure about its answer, but not so hard that it fails entirely. If the model already answers it correctly every time, there’s no uncertainty to minimize. And if the model fails outright, entropy minimization could reinforce incorrect reasoning. The researchers tested tens of thousands of model–prompt pairs to find effective examples, highlighting a potential challenge in operationalizing EM: it requires careful prompt selection for effective gains.
Performance Gains Without Labels
The reported improvements are substantial. In controlled tests across over 13,000 models, EM boosted accuracy on math reasoning tasks by as much as 30 percentage points. That level of improvement rivals or exceeds what’s been achieved with full RLHF pipelines, all without a single labeled example. What makes the result even more notable is the speed—it takes minutes, not days or weeks. The experiments suggest EM doesn’t just reinforce rote answers; it seems to improve general reasoning patterns, at least within the tested domains.
Potential Impact and Limitations
EM’s appeal is obvious: low-cost, low-labor, and fast improvements to existing models. It could be especially valuable in scenarios where labeled data is scarce, such as low-resource languages or sensitive domains like medicine and law. But it’s not without risk. A poorly chosen prompt could make a model confidently wrong. Overconfidence is a known failure mode in LLMs, and EM might exacerbate that if not properly managed. The authors acknowledge these concerns and suggest that future research should explore safeguards and broader task applicability.
Looking Ahead
Ubiquant has open-sourced the EM code, encouraging the community to test and adapt the approach. It’s too early to say whether entropy minimization will replace or merely supplement more established training methods like RLHF, but the early signs are promising. If EM can be reliably applied across different tasks and models, it could change the economics of post-training for AI systems—making high-performance reasoning more accessible without the bottleneck of human labels.
In a field where bigger and more expensive has often equaled better, EM offers a minimalist alternative: trust what the model already knows, and teach it to trust itself.
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