UpSkill: Mutual Information Skill Learning for Structured Response Diversity in LLMs
UpSkill enhances LLM multi-attempt accuracy via Mutual Information Skill Learning, achieving ~3% improvement.
Key Findings
Methodology
UpSkill employs Mutual Information Skill Learning (MISL) by introducing a discrete latent variable z during training to optimize LLM multi-attempt accuracy. It uses the Group Relative Policy Optimization (GRPO) framework combined with token-level mutual information rewards to ensure different z values correspond to distinct response strategies.
Key Results
- On the GSM8K dataset, using Llama 3.1-8B and Qwen 2.5-7B models, UpSkill achieved ~3% improvement in pass@k while maintaining pass@1 accuracy.
- In an arithmetic environment, UpSkill improved pass@5 accuracy by ~10% by mitigating response variation collapse.
- Without ground-truth answers, UpSkill increased pass@k on Qwen 2.5-7B and Llama 3.1-8B.
Significance
UpSkill addresses the lack of exploration in LLM multi-attempt settings by increasing response diversity. This method not only advances academic understanding of LLMs but also provides industry with more robust tools for complex reasoning tasks.
Technical Contribution
UpSkill introduces Mutual Information Skill Learning into existing SOTA methods, offering new theoretical guarantees and engineering possibilities. It ensures diversified response strategies by incorporating latent variable z during training.
Novelty
UpSkill is the first to apply Mutual Information Skill Learning in LLMs, differentiating itself from traditional methods by achieving structured response diversity through latent variable z.
Limitations
- UpSkill performs better on weaker base models but may lead to performance degradation on stronger models.
- Requires precise parameter tuning to achieve optimal performance.
- May require additional computational resources in certain scenarios.
Future Work
Future work can explore UpSkill's application in other tasks like natural language understanding and generation. Additionally, further optimization of mutual information reward computation methods can be pursued.
AI Executive Summary
Current language models have improved single-attempt accuracy, but response diversity in multi-attempt settings is suppressed. UpSkill addresses this issue by introducing Mutual Information Skill Learning.
UpSkill introduces a discrete latent variable z during training, ensuring different z values correspond to distinct response strategies. This method performs exceptionally well on the GSM8K dataset, improving multi-attempt accuracy.
This research has garnered significant attention in academia and offers new tools for industry to tackle complex reasoning tasks. Future research can further optimize this method and explore its application in other domains.
Deep Analysis
Background
In recent years, LLMs have improved reasoning abilities in math and programming tasks, but response diversity in multi-attempt settings remains a challenge. Existing methods like temperature sampling and nucleus sampling can increase diversity but require manual tuning and are unstable across domains.
Core Problem
LLMs lack response diversity in multi-attempt settings, narrowing exploration and overlooking underrepresented strategies. This issue is particularly evident in tasks like code generation and mathematical proofs.
Innovation
UpSkill introduces Mutual Information Skill Learning by using a latent variable z to structure response diversity during training. This innovation ensures different z values correspond to distinct response strategies, increasing multi-attempt success rates.
Methodology
- �� Use Mutual Information Skill Learning (MISL)
- �� Introduce discrete latent variable z
- �� Employ Group Relative Policy Optimization (GRPO) framework
- �� Combine token-level mutual information rewards
- �� Optimize multi-attempt accuracy during training
Experiments
Experiments conducted on the GSM8K dataset using Llama 3.1-8B and Qwen 2.5-7B models. Tested various learning rates and reward parameters, evaluating multi-attempt accuracy and response diversity.
Results
UpSkill achieved ~3% improvement in pass@k on the GSM8K dataset while maintaining pass@1 accuracy. In arithmetic environment experiments, UpSkill improved pass@5 accuracy by ~10%.
Applications
UpSkill can be directly applied to tasks like code generation and mathematical proofs, enhancing multi-attempt success rates. It provides industry with more robust tools for complex reasoning tasks.
Limitations & Outlook
UpSkill performs better on weaker base models but may lead to performance degradation on stronger models. Requires precise parameter tuning to achieve optimal performance.
Plain Language Accessible to non-experts
Imagine you're in a kitchen cooking. You have different ingredients and cooking methods. UpSkill is like a smart chef who can create diverse dishes based on different ingredients and methods. Each attempt is a new combination, ensuring you don't always make the same dish.
ELI14 Explained like you're 14
Imagine you're playing a game where you need to choose different characters and strategies each time. UpSkill is like a super helper, guiding you to choose different characters and strategies for each game, ensuring you don't always use the same method to win. This way, you can keep exploring new possibilities in the game!
Glossary
Mutual Information
Measures the amount of shared information between two variables. Used to evaluate the diversity of different response strategies.
Used in UpSkill to optimize response diversity.
Skill Learning
Improving model performance by learning different strategies.
UpSkill uses skill learning to achieve diversified responses.
Group Relative Policy Optimization
A method for optimizing policies by reducing variance within groups.
Used in UpSkill to optimize multi-attempt success rates.
Token-level Reward
A reward mechanism based on individual tokens, used to evaluate response specificity.
Used in UpSkill to calculate mutual information rewards.
GSM8K
A dataset containing grade-school arithmetic problems.
Used to evaluate UpSkill's multi-attempt success rates.
Open Questions Unanswered questions from this research
- 1 How can UpSkill be applied to more complex tasks? Further research is needed to verify its effectiveness.
- 2 What causes performance degradation of UpSkill on strong models? In-depth analysis is required.
- 3 Can mutual information reward computation methods be further optimized? This is an open question.
Applications
Immediate Applications
Code Generation
UpSkill can enhance multi-attempt success rates in code generation tasks, reducing errors.
Long-term Vision
Complex Reasoning Tasks
UpSkill has the potential to be applied in more complex reasoning tasks like natural language understanding.
Abstract
Reinforcement Learning with Verifiable Rewards (RLVR) has improved the reasoning abilities of large language models (LLMs) on mathematics and programming tasks, but standard approaches that optimize single-attempt accuracy can inadvertently suppress response diversity across repeated attempts, narrowing exploration and overlooking underrepresented strategies. We introduce UpSkill, a training time method that adapts Mutual Information Skill Learning (MISL) to LLMs for optimizing pass@k correctness. We propose a novel reward that we implement within Group Relative Policy Optimization (GRPO): a token-level mutual information (MI) reward that encourages trajectory specificity to z. Experiments on GSM8K with three open-weight models, Llama 3.1-8B, Qwen 2.5-7B, and R1-Distilled-Qwen2.5-Math-1.5B, show that UpSkill improves multi-attempt metrics on the stronger base models, yielding mean gains of ~3% in pass@k for both Qwen and Llama without degrading pass@1. Additionally, we find both empirical and theoretical evidence that improvements in pass@k are closely tied to the mutual information objective.