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TIPS: Turn-Level Information-Potential Reward Shaping for Search-Augmented LLMs

arXiv:2603.22293v1 Announce Type: new Abstract: Search-augmented large language models (LLMs) trained with reinforcement learning (RL) have achieved strong results on open-domain question answering (QA), but training still remains a significant challenge. The optimization is often unstable due to sparse rewards and difficult credit assignments across reasoning and tool calls. To address this, we introduce Turn-Level Information Potential Reward Shaping (TIPS), a simple framework that assigns dense, turn-level rewards to each reasoning + tool-call segment based on the increased likelihood of the correct answer under a teacher model. By leveraging the potential-based reward shaping, TIPS offers fine-grained and policy-invariant guidance that overcomes the limitations of outcome-only optimization. Evaluated on seven QA benchmarks, TIPS consistently outperforms GRPO/PPO baselines and substantially improves training stability. For instance, with a Qwen-2.5 7B Instruct model, TIPS improves

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Yutao Xie, Nathaniel Thomas, Nicklas Hansen, Yang Fu, Li Erran Li, Xiaolong Wang · · 1 min read · 2 views
#cs.CL #cs.AI #cs.LG

arXiv:2603.22293v1 Announce Type: new Abstract: Search-augmented large language models (LLMs) trained with reinforcement learning (RL) have achieved strong results on open-domain question answering (QA), but training still remains a significant challenge. The optimization is often unstable due to sparse rewards and difficult credit assignments across reasoning and tool calls. To address this, we introduce Turn-Level Information Potential Reward Shaping (TIPS), a simple framework that assigns dense, turn-level rewards to each reasoning + tool-call segment based on the increased likelihood of the correct answer under a teacher model. By leveraging the potential-based reward shaping, TIPS offers fine-grained and policy-invariant guidance that overcomes the limitations of outcome-only optimization. Evaluated on seven QA benchmarks, TIPS consistently outperforms GRPO/PPO baselines and substantially improves training stability. For instance, with a Qwen-2.5 7B Instruct model, TIPS improves the average Exact Match score by 11.8% and F1 by 13.6% relative to PPO. Our results demonstrate that turn-level information-potential reward shaping provides an effective and general solution to sparse-reward credit assignment for multi-turn LLM reasoning.

Executive Summary

This article proposes Turn-Level Information-Potential Reward Shaping (TIPS), a novel framework for training search-augmented large language models (LLMs) with reinforcement learning (RL) on open-domain question answering (QA) tasks. TIPS addresses the challenges of sparse rewards and difficult credit assignments by assigning dense, turn-level rewards based on the increased likelihood of the correct answer under a teacher model. The authors evaluate TIPS on seven QA benchmarks and demonstrate significant improvements in training stability and performance compared to GRPO/PPO baselines. The results suggest that TIPS provides an effective and general solution to sparse-reward credit assignment for multi-turn LLM reasoning. The article contributes to the ongoing research in improving the performance and stability of LLMs in QA tasks.

Key Points

  • ▸ TIPS is a novel framework for training search-augmented LLMs on open-domain QA tasks
  • ▸ TIPS addresses the challenges of sparse rewards and difficult credit assignments
  • ▸ TIPS demonstrates significant improvements in training stability and performance compared to GRPO/PPO baselines

Merits

Effective solution to sparse-reward credit assignment

TIPS provides a fine-grained and policy-invariant guidance that overcomes the limitations of outcome-only optimization

Improved training stability

TIPS consistently outperforms GRPO/PPO baselines on seven QA benchmarks

General applicability

TIPS is applicable to multi-turn LLM reasoning and can be used for various QA tasks

Demerits

Limited evaluation on diverse datasets

The authors evaluate TIPS on seven QA benchmarks, but it is unclear whether the results generalize to diverse datasets

Dependence on teacher models

TIPS relies on teacher models to assign rewards, which may limit its applicability in real-world scenarios

Expert Commentary

The article presents a novel and effective solution to sparse-reward credit assignment in RL for LLMs. The evaluation on seven QA benchmarks demonstrates significant improvements in training stability and performance compared to GRPO/PPO baselines. However, the dependence on teacher models and limited evaluation on diverse datasets are potential limitations that need to be addressed. The development of TIPS highlights the importance of addressing sparse-reward credit assignment in RL for LLMs, which can inform policy decisions in the development of AI models. Overall, the article contributes to the ongoing research in improving the performance and stability of LLMs in QA tasks.

Recommendations

  • ✓ Future research should investigate the applicability of TIPS to diverse datasets and real-world scenarios
  • ✓ The development of teacher models that can provide accurate and informative rewards is crucial for the widespread adoption of TIPS

Sources

Original: arXiv - cs.CL

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