Sample Where You Struggle: Sharpening Base Model Reasoning via Entropy-Guided Power Sampling
arXiv:2606.09926v1 Announce Type: new Abstract: Sampling from the sequence-level power distribution $p^\alpha$ elicits RL-level reasoning from base language models without any parameter updates, but the standard Metropolis--Hastings (MH), a Markov Chain Monte Carlo (MCMC) sampler, is both expensive and slow-mixing. We trace both to a structural mismatch: $p^\alpha$ mainly departs from $p$ at a sparse, spatially clustered set of high-entropy decision points, yet MH proposes resampling positions uniformly along the prefix -- wasting compute on near-degenerate conditionals while under-mixing prec
The continuous drive for more efficient and robust AI reasoning capabilities necessitates innovation in sampling methods to overcome computational bottlenecks.
Improving the efficiency of sampling from power distributions can significantly enhance the reasoning abilities of large language models without extensive retraining, democratizing access to more sophisticated AI.
This research suggests a more effective method for eliciting high-level reasoning from existing base models, potentially leading to faster development cycles and improved AI agent performance.
- · AI developers
- · Cloud compute providers
- · Companies utilizing advanced AI models
More efficient power sampling will lead to better performance for AI models, especially in complex reasoning tasks.
Improved reasoning capabilities could accelerate the development and deployment of autonomous AI agents across various sectors.
This efficiency gain may reduce the computational cost of deploying advanced AI, potentially lowering barriers to entry for smaller firms.
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Read at arXiv cs.LG