# SIGHT: Reinforcement Learning with Self-Evidence and Information-Gain Diverse Branching for Search Agent

arXiv: https://arxiv.org/abs/2602.11551 · HTML: https://mlsanity.com/paper/2602.11551/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning.
- Builds on 17 of its 33 references that are in the map.
- First posted on arXiv 12 Feb 2026.

## Placed in

- LLM reasoning / [Knowledge- and tool-augmented reasoning](https://mlsanity.com/lr/knowledge-and-tool-augmented-reasoning/)
- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 17 of 17)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning](https://mlsanity.com/paper/2503.09516/) (2025)
- [Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps](https://mlsanity.com/paper/2011.01060/) (2020)
- [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://mlsanity.com/paper/2212.03533/) (2022)
- [Measuring and Narrowing the Compositionality Gap in Language Models](https://mlsanity.com/paper/2210.03350/) (2022)
- [MuSiQue: Multihop Questions via Single-hop Question Composition](https://mlsanity.com/paper/2108.00573/) (2021)
- [WebSailor: Navigating Super-human Reasoning for Web Agent](https://mlsanity.com/paper/2507.02592/) (2025)
- [Deep Research: A Survey of Autonomous Research Agents](https://mlsanity.com/paper/2508.12752/) (2025)
- [Search and Refine During Think: Facilitating Knowledge Refinement for Improved Retrieval-Augmented Reasoning](https://mlsanity.com/paper/2505.11277/) (2025)
- [ParallelMuse: Agentic Parallel Thinking for Deep Information Seeking](https://mlsanity.com/paper/2510.24698/) (2025)
- [Search-R2: Enhancing Search-Integrated Reasoning via Actor-Refiner Collaboration](https://mlsanity.com/paper/2602.03647/) (2026)
- [WebAnchor: Anchoring Agent Planning to Stabilize Long-Horizon Web Reasoning](https://mlsanity.com/paper/2601.03164/) (2026)
- [LRAS: Advanced Legal Reasoning with Agentic Search](https://mlsanity.com/paper/2601.07296/) (2026)
- [Reinforcement Learning for Tool-Integrated Interleaved Thinking towards Cross-Domain Generalization](https://mlsanity.com/paper/2510.11184/) (2025)
- [CARL: Criticality-Aware Agentic Reinforcement Learning](https://mlsanity.com/paper/2512.04949/) (2025)

## Built on by (most cited first, 3 of 3)

- [TopoCurate:Modeling Interaction Topology for Tool-Use Agent Training](https://mlsanity.com/paper/2603.01714/) (2026)
- [ARBOR: Online Process Rewards via a Reusable Rubric Buffer for Search Agents](https://mlsanity.com/paper/2606.03239/) (2026)
- [IGSD: Environment-Verified Hindsight Self-Distillation for Search Agents](https://mlsanity.com/paper/2609.32694/) (2026)

## Abstract (arXiv)

Reinforcement Learning (RL) has empowered Large Language Models (LLMs) to master autonomous search for complex question answering. However, particularly within multi-turn search scenarios, this interaction introduces a critical challenge: search results often suffer from high redundancy and low signal-to-noise ratios. Consequently, agents easily fall into "Tunnel Vision," where the forced interpretation of early noisy retrievals leads to irreversible error accumulation. To address these challenges, we propose SIGHT, a framework that enhances search-based reasoning through Self-Evidence Support (SES) and Information-Gain Driven Diverse Branching. SIGHT distills search results into high-fidelity evidence via SES and calculates an Information Gain score to pinpoint pivotal states where observations maximally reduce uncertainty. This score guides Dynamic Prompting Interventions - including de-duplication, reflection, or adaptive branching - to spawn new branches with SES. Finally, by integrating SES and correctness rewards via Group Relative Policy Optimization, SIGHT internalizes robust exploration strategies without external verifiers. Experiments on single-hop and multi-hop QA benchmarks demonstrate that SIGHT significantly outperforms existing approaches, particularly in complex reasoning scenarios, using fewer search steps.