Prioritize the Process, Not Just the Outcome: Rewarding Latent Thought Trajectories Improves Reasoning in Looped Language Models
arXiv:2602.10520v3 Announce Type: replace Abstract: Looped Language Models (LoopLMs) perform multi-step latent reasoning prior to token generation and outperform conventional LLMs on reasoning benchmarks at smaller parameter budgets. However, attempts to further improve LoopLM reasoning with reinforcement learning have failed - standard objectives such as Group Relative Policy Optimization (GRPO) only assign credit to the final latent state, creating a fundamental mismatch with the model's internal computation. To resolve this, we introduce RLTT (Reward Latent Thought Trajectories), a reinforc
The paper directly addresses known limitations of current reinforcement learning techniques when applied to chained reasoning in language models, representing a new advancement in AI methodology.
This research could significantly improve the efficiency and reasoning capabilities of AI models, particularly at lower computational costs, making advanced AI more accessible.
The proposed RLTT method alters how reinforcement learning optimizes multi-step reasoning in language models, moving beyond just outcomes to reward the internal thought process.
- · AI research institutions
- · Developers of smaller, more efficient LLMs
- · Sectors requiring complex AI reasoning
- · Developers relying solely on outcome-based RL for complex AI tasks
- · Current generation of less efficient large language models
Improved performance and broader application of more parameter-efficient AI reasoning models.
Reduced computational barriers to entry for developing powerful AI, potentially democratizing advanced AI capabilities.
Acceleration of multi-agent and complex autonomous AI systems as reasoning becomes more robust and efficient.
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