FORT synthesizes shortcut-resistant search tasks by controlling four identified shortcut risks across entity selection, graph construction, question formulation, and refinement, producing training data that yields agents with longer search trajectories and top performance among open-source models on
Chaining the evidence: Robust reinforcement learning for deep search agents with citation-aware rubric rewards
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 6verdicts
UNVERDICTED 6representative citing papers
CHERRL is a new controllable testbed for reproducing, analyzing, and detecting reward hacking in rubric-based RL by injecting known biases into LLM-as-a-Judge systems.
LongTraceRL trains LLMs on long-context reasoning by generating tiered-distractor data from search trajectories and using positive-only entity rubric rewards for process supervision.
REFLECT benchmark shows current LLM judges achieve below 55% accuracy detecting failures in evidence-based research agents, especially on evidence verification.
GDCR assigns step-level rewards via distance to the answer node in a training-time ER graph and SAPO combines these with trajectory advantages for credit assignment in agentic search.
MetaResearcher is a proposed multi-component framework for scaling deep research agent training via adversarial virtual worlds, discovery tasks, meta-rewards, and multi-agent collaboration.
citing papers explorer
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FORT-Searcher: Synthesizing Shortcut-Resistant Search Tasks for Training Deep Search Agents
FORT synthesizes shortcut-resistant search tasks by controlling four identified shortcut risks across entity selection, graph construction, question formulation, and refinement, producing training data that yields agents with longer search trajectories and top performance among open-source models on
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Reproducing, Analyzing, and Detecting Reward Hacking in Rubric-Based Reinforcement Learning
CHERRL is a new controllable testbed for reproducing, analyzing, and detecting reward hacking in rubric-based RL by injecting known biases into LLM-as-a-Judge systems.
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LongTraceRL: Learning Long-Context Reasoning from Search Agent Trajectories with Rubric Rewards
LongTraceRL trains LLMs on long-context reasoning by generating tiered-distractor data from search trajectories and using positive-only entity rubric rewards for process supervision.
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Time to REFLECT: Can We Trust LLM Judges for Evidence-based Research Agents?
REFLECT benchmark shows current LLM judges achieve below 55% accuracy detecting failures in evidence-based research agents, especially on evidence verification.
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Beyond Trajectory Rewards: Step-level Credit Assignment for Agentic Search via Graph Modeling
GDCR assigns step-level rewards via distance to the answer node in a training-time ER graph and SAPO combines these with trajectory advantages for credit assignment in agentic search.
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MetaResearcher: Scaling Deep Research via Self-Reflective Reinforcement Learning in Adversarial Virtual Environments
MetaResearcher is a proposed multi-component framework for scaling deep research agent training via adversarial virtual worlds, discovery tasks, meta-rewards, and multi-agent collaboration.