TreeMem assigns credit to agents in multi-agent memory systems by expanding outputs into a tree and using Monte Carlo averaging of final rewards to optimize each agent's policy.
Multi-agent deep research: Training multi-agent systems with m-grpo
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
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2026 7roles
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background 2representative citing papers
SOLAR-RL assigns dense step-level rewards from static trajectory data by detecting first failure points and applying target-aligned shaping to improve long-horizon GUI task completion without full online interactions.
CoMAM jointly optimizes agents in multi-agent LLM memory systems via end-to-end RL and adaptive credit assignment to improve collaboration and performance.
A survey of credit assignment techniques in LLM reinforcement learning that distinguishes maturing methods for reasoning from new approaches needed for agentic settings and provides supporting resources.
This survey organizes RL for LLM multi-agent systems into reward families, credit units, and five orchestration sub-decisions, notes the absence of explicit stopping-decision training in its paper pool, and releases a tagged corpus.
The survey structures agentic reasoning for LLMs into foundational, self-evolving, and collective multi-agent layers while distinguishing in-context orchestration from post-training optimization and reviewing applications across domains.
citing papers explorer
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Tree-based Credit Assignment for Multi-Agent Memory System
TreeMem assigns credit to agents in multi-agent memory systems by expanding outputs into a tree and using Monte Carlo averaging of final rewards to optimize each agent's policy.
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SOLAR-RL: Semi-Online Long-horizon Assignment Reinforcement Learning
SOLAR-RL assigns dense step-level rewards from static trajectory data by detecting first failure points and applying target-aligned shaping to improve long-horizon GUI task completion without full online interactions.
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Joint Optimization of Multi-agent Memory System
CoMAM jointly optimizes agents in multi-agent LLM memory systems via end-to-end RL and adaptive credit assignment to improve collaboration and performance.
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From Reasoning to Agentic: Credit Assignment in Reinforcement Learning for Large Language Models
A survey of credit assignment techniques in LLM reinforcement learning that distinguishes maturing methods for reasoning from new approaches needed for agentic settings and provides supporting resources.
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Reinforcement Learning for LLM-based Multi-Agent Systems through Orchestration Traces
This survey organizes RL for LLM multi-agent systems into reward families, credit units, and five orchestration sub-decisions, notes the absence of explicit stopping-decision training in its paper pool, and releases a tagged corpus.
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Agentic Reasoning for Large Language Models
The survey structures agentic reasoning for LLMs into foundational, self-evolving, and collective multi-agent layers while distinguishing in-context orchestration from post-training optimization and reviewing applications across domains.
- Learning Decentralized LLM Collaboration with Multi-Agent Actor Critic