Revolutionizing LLM Reasoning: Likelihood-Based Rewards Show Promise!
Analysis
This research introduces a novel approach to improve the reasoning capabilities of Large Language Models (LLMs) using likelihood-based reward functions. It's exciting to see how these rewards, derived from the probability of generating the correct answer, can potentially outperform traditional methods, particularly in complex scenarios.
Key Takeaways
- •Likelihood-based rewards, derived from answer probabilities, are explored as an alternative to standard binary rewards.
- •The log-probability of the correct answer proved highly effective for Chain of Thought learning.
- •These new rewards show promise in verifiable and non-verifiable reasoning settings.
Reference / Citation
View Original"We find that using the log-probability of the reference answer as the reward for chain-of-thought (CoT) learning is the only option that performs well in all setups."
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ArXiv NLPFeb 5, 2026 05:00
* Cited for critical analysis under Article 32.