REVIEW 16 cited by
An Empirical Study on Reinforcement Learning for Reasoning-Search Interleaved LLM Agents
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Reinforcement learning (RL) has demonstrated strong potential in training large language models (LLMs) capable of complex reasoning for real-world problem solving. More recently, RL has been leveraged to create sophisticated LLM-based search agents that adeptly combine reasoning with search engine use. While the use of RL for training search agents is promising, the optimal design of such agents remains not fully understood. In particular, key factors -- such as (1) reward formulation, (2) the choice and characteristics of the underlying LLM, and (3) the role of the search engine in the RL process -- require further investigation. In this work, we conduct comprehensive empirical studies to systematically investigate these and offer actionable insights. We highlight several key findings: format rewards are effective in improving final performance, whereas intermediate retrieval rewards have limited impact; the scale and initialization of the LLM (general-purpose vs. reasoning-specialized) significantly influence RL outcomes; and the choice of search engine plays a critical role in shaping RL training dynamics and the robustness of the trained agent during inference. These establish important guidelines for successfully building and deploying LLM-based search agents in real-world applications. Code is available at https://github.com/PeterGriffinJin/Search-R1.
Forward citations
Cited by 16 Pith papers
-
LatentRAG: Latent Reasoning and Retrieval for Efficient Agentic RAG
LatentRAG performs agentic RAG by generating latent tokens for thoughts and subqueries in one forward pass, matching explicit methods' accuracy on seven benchmarks while reducing latency by ~90%.
-
TCPO: Turn-Level Credit Policy Optimization
TCPO improves multi-turn LLM RL by converting verifier scores into turn-level credit via three reference-based comparisons, beating baseline methods on math, code, and agent tasks.
-
Verbal-R3: Verbal Reranker as the Missing Bridge between Retrieval and Reasoning
Verbal-R3 uses a verbal reranker to generate analytic narratives that guide retrieval and reasoning in LLMs, achieving SOTA results on complex QA benchmarks.
-
$S^3$-R1: Learning to Retrieve and Answer Step-by-Step with Synthetic Data
S^3-R1 generates synthetic intermediate-difficulty multi-hop questions and applies dense rewards for search quality plus answer correctness, yielding up to 10% better out-of-domain generalization than baselines.
-
TeaRAG: A Token-Efficient Agentic Retrieval-Augmented Generation Framework
TeaRAG shows that hybrid chunk+triplet retrieval with Personalized PageRank and an iterative process-aware DPO reward keeps QA accuracy while cutting reasoning tokens by roughly 60%.
-
Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search
SLIM separates search and browse tools and summarizes trajectories every 50 turns, beating several open-source agentic search systems on BrowseComp and HLE with fewer tool calls and lower cost.
-
Beyond Correctness: Rewarding Faithful Reasoning in Retrieval-Augmented Generation
A turn-level faithfulness reward improves a Search-R1-style agent's Information-Think and Think-Answer faithfulness as judged by the same reward model used for training, while task accuracy is roughly unchanged.
-
SSRL: Self-Search Reinforcement Learning
SSRL, a training pipeline that uses an LLM's own repeated sampling as a search environment for RL, improves question answering without external tools and transfers to real search engines.
-
BrowseComp-Plus: A More Fair and Transparent Evaluation Benchmark of Deep-Research Agent
BrowseComp-Plus is a fixed, human-verified document corpus with supporting and negative documents that allows controlled, reproducible evaluation of deep-research agents and their retrievers.
-
MetaAgent: Toward Self-Evolving Agent via Tool Meta-Learning
The paper advertises a self-evolving tool-using agent that beats baselines, but the body contains a different math paper, leaving the performance claims unsupported.
-
TRACE: Turn-level Reward Assignment via Credit Estimation for Long-Horizon Agents
Turn-level rewards from TD changes in a frozen reference model's gold-answer log-probability improve long-horizon search-agent RL on closed- and open-web benchmarks.
-
ReasonBENCH: Benchmarking the (In)Stability of LLM Reasoning
LLM reasoning benchmark scores vary substantially across repeated runs under the same model, strategy, and task, so single-run evaluation can misrank systems.
-
Erase to Improve: Erasable Reinforcement Learning for Search-Augmented LLMs
ERL trains LLMs to erase faulty reasoning steps and regenerate them in place, yielding gains of up to 8.48% EM on multi-hop QA benchmarks like HotpotQA.
-
DocArena: Turning Raw Documents into Controllable Training Environments for Document Search Agents
DocArena automates creation of multimodal document QA training data via MLLM-based structuring and cross-page reasoning pairs, yielding agents with top retrieval and QA performance in unified tests.
-
$S^3$-R1: Learning to Retrieve and Answer Step-by-Step with Synthetic Data
S^3-R1 generates synthetic multi-hop questions and uses combined intermediate and final rewards to train RL models for retrieval and answering, reporting up to 10% better out-of-domain generalization.
-
A Survey of Reinforcement Learning for Large Reasoning Models
A survey compiling RL methods, challenges, data resources, and applications for enhancing reasoning in large language models and large reasoning models since DeepSeek-R1.
Discussion (0). Sign in to comment.