Reinforcement Learning (RL) is transforming large language models (LLMs) beyond basic language understanding, enabling them to evolve into sophisticated conversationalists, knowledgeable assistants, and reasoning experts. Just as a child learns to reason through school and real-world interactions, RL acts as a "school" for LLMs, refining their decision-making and problem-solving skills through iterative feedback.

RL drives progress in three key LLM roles:

1. Conversationalist: Engaging in dynamic, context-aware dialogue.
2. Knowledgeable Assistant: Providing accurate, encyclopedic advice.
3. Reasoning Expert: Solving complex problems through logical deduction, abduction, and induction.

For a deeper look at RL’s evolution, see my previous post on policy optimization: From REINFORCE to GRPO: Evolution of Policy Optimization in Reinforcement Learning

Let’s explore two recent breakthroughs that showcase how RL is being developed to improve reasoning in LLM.

RL’s Cutting-Edge Advances

Absolute Zero: Reinforced Self-play Reasoning with Zero Data

Source: Absolute Zero: Reinforced Self-play Reasoning with Zero Data

General Idea of AZR

AZR (Absolute Zero Reasoner) is a novel RL framework enabling LLMs to enhance reasoning without human-curated data.

• Zero External Data: Trains on self-generated code-based tasks.
• Code as Environment: Uses Python execution for reliable task validation.
• Three Reasoning Modes: Deduction, abduction, induction.
• Self-Evolving Curriculum: Proposes learnable, balanced tasks.
• Dual Roles: A single LLM alternates as Proposer (generates tasks, rewarded for learnability) and Solver (solves tasks, rewarded for accuracy). This self-play approach drives autonomous reasoning improvement.

MiniMax-M1: CISPO-Powered Reasoning

Source: MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention

The open-source MiniMax-M1 model rivals top LLMs in reasoning while being cost-efficient, thanks to RL innovations like CISPO (Clipped Importance Sampling Policy Optimization).

What’s New in CISPO:

• Clipping: Unlike GRPO, which may discard rare but meaningful tokens (e.g., However, Recheck), CISPO clips importance weights, preserving learning signals for better reasoning.
• Faster Learning: Matches DAPO’s performance with 50% fewer training steps, outperforming GRPO in reward learning speed.
• Stable Off-Policy Updates: Supports up to 16 off-policy updates without instability, enabling efficient data reuse.
• No KL Penalty Tuning: CISPO eliminates the need to tune the weight of the KL divergence penalty, simplifying training while maintaining stability through clipped importance sampling.

Conclusion

RL is the backbone of LLM reasoning, pushing models to think like researchers and specialists. Like a child mastering critical thinking through experience, LLMs leverage RL to navigate complex tasks. With innovations like AZR and CISPO, RL continues to evolve, paving the way for LLMs to tackle advanced challenges like scientific discovery and creative problem-solving in the future.