Training Advisors for LLM Agents from Task Outcomes
cs.CL, cs.AI, cs.LG
Submitted: 2026-10-07
Updated: 2026-10-07
Sources
- How to Train Your Advisor: Steering Black-Box LLMs with Advisor Models
- Training Verifiers to Solve Math Word Problems
- Steer, Don't Solve: Training Small Critic Models for Large Code Agents
- Agent Reinforcement Learning via Pivotal-Aware Self-Feedback Retry
- Co-Evolving Actor-Conditioned Critics for Non-Verifiable Generation
- ICRL: Learning to Internalize Self-Critique with Reinforcement Learning
- DeepDive: Advancing Deep Search Agents with Knowledge Graphs and Multi-Turn RL
- Improve Mathematical Reasoning in Language Models by Automated Process Supervision
- DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
- Solving math word problems with process- and outcome-based feedback
- Generating Sequences by Learning to Self-Correct
- AgentPRM: Process Reward Models for LLM Agents via Step-Wise Promise and Progress
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