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Notes on policy optimization
9 notes
Contextual Information Policy Optimization for Search Agents
The paper proposes a new method called Contextual Information Policy Optimization (CIPO) for search agents. This method aims to improve the efficiency of evidence use in complex or evolving information tasks by…
Contextual Information Policy Optimization for Search Agents
The paper proposes a new method called Contextual Information Policy Optimization (CIPO) to improve the reliability of search agents for complex or evolving information tasks. CIPO aligns policy optimization with…
PATS: Policy-Aware Training Scaffolding for Agentic Reinforcement Learning
The authors propose a new training paradigm called PATS for reinforcement learning agents that learn long-term tasks. PATS converts rollout groups from the latest policy into evidence cards and adjusts the context used…
PATS: Policy-Aware Training Scaffolding for Agentic Reinforcement Learning
The paper 'PATS: Policy-Aware Training Scaffolding for Agentic Reinforcement Learning' proposes a new training paradigm called Pats, which aims to improve the performance of long-horizon reinforcement learning agents…
Active Inference as a Convex Markov Decision Process
The paper presents Active Inference (AIF) as a method that minimizes the expected free energy (EFE), combining both epistemic and pragmatic objectives within a single variational principle. The authors frame AIF as…
Co-Evolving LLM Evaluators and Policies via DynamicRubric
The researchers propose DynamicRubric, a method that allows large language models to improve by co-evolving evaluators and policies. This method generates weighted binary rubrics for each candidate set and aggregates…
Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents
The paper proposes a new method called Information Gain-based Rollout Policy Optimization (IGRPO) for improving large language model (LLM) agents in long-horizon search tasks. The method addresses the issue of existing…
TREK: Distill to Explore, Reinforce to Refine
TREK is a procedure that uses distillation for exploration support expansion to improve policy optimization on tasks where the current policy stalls on hard prompts. It identifies low-performing prompts, generates…
Semantic Consistency Policy Optimization for Reinforcement Learning of LLM Agents
The researchers propose a method called Semantic Consistency Policy Optimization (SCPO) to address a problem in reinforcement learning of LLM agents. The issue is that these agents are trained for long-term, sparse…
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