Evolving Language Models without Labels: Majority Drives Selection, Novelty Promotes Variation

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Paper Summary

Conflicts of Interest

Identified Weaknesses

Rating Explanation

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Paper Summary

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Majority-Vote-Only Training Makes LLMs Boring and Dumb: EVOL-RL Keeps Them Smart and Interesting

This paper introduces EVOL-RL, a new method for training large language models without labeled data. It addresses the "entropy collapse" problem in existing label-free methods, where models become less creative and get stuck in repetitive patterns by balancing selection with variation. EVOL-RL improves performance across various math reasoning tasks and generalizes better to new tasks.

Possible Conflicts of Interest

The authors are affiliated with Tencent AI Lab, which could potentially bias the selection of models and datasets used in the experiments. However, the paper seems to present a balanced comparison against existing baseline methods.

Identified Weaknesses

Limited Benchmarking

While the paper tests EVOL-RL on several mathematical and reasoning datasets, it would strengthen the conclusions to evaluate its performance on a wider range of tasks, including those involving natural language generation, common sense reasoning, or other domains beyond math and logical problem-solving.

Computational Cost

The paper mentions using 64 samples per instance, and a smaller subset of 32 for update. This sampling and generation process likely carries a significant computational cost, especially for larger models or more complex tasks, making it potentially less accessible for researchers with limited resources. A discussion of the computational demands and potential optimizations would be beneficial.

Novelty Calculation

The calculation of novelty uses mean and max cosine similarity across embedding vectors, which might not capture the diversity of the reasoning chains. The paper provides only some intuitive explanations of why this particular method was chosen over others, but not a rigorous comparison of alternative metrics (e.g. edit distance, Jaccard index, etc).

Comparison with other Diversity-Promoting Techniques

The paper does not extensively discuss or compare EVOL-RL with existing methods for promoting diversity in language models, especially in the reinforcement learning setting. A more comprehensive comparison would enhance the paper's contributions.

Limited Evaluation of Adaptability

The paper mentions adaptability as a potential benefit of EVOL-RL, but it doesn't provide dedicated experiments or analysis to assess how well the model adapts to new or unseen tasks, or if EVOL-RL leads to performance gains in continual learning scenarios.

Rating Explanation

This paper proposes a novel and promising method for label-free training of LLMs that addresses a significant limitation of existing approaches. The results are impressive, showing strong gains in both performance and generalization. The paper's rating is slightly reduced due to the limited benchmarking domain and other limitations like novelty calculation methods. More detailed ablation studies, and further exploration of this direction seems promising.

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