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Posted: 2025-04-16 09:14:38 UTC

This article contains some claims that remain unverified. While much of the content may be accurate, exercise care when relying on this information.
This article contains some claims that remain unverified. While much of the content may be accurate, exercise care when relying on this information.
Status
Last Updated
2025-04-16 09:15:03 UTC
Verified By
Rollup News
A new survey paper on ArXiv summarizes the progress in post-training and reasoning Large Language Models (LLMs). It explores how fine-tuning, reinforcement learning, and test-time strategies enhance LLMs' reasoning, factual accuracy, and alignment.
Post-training techniques for LLMs
Fine-tuning strategies
Reinforcement learning
Test-time strategies
Improving reasoning
Enhancing factual accuracy
Model alignment
Catastrophic forgetting
Reward hacking
Inference-time trade-offs