REVIEW 5 cited by
Multiagent Finetuning: Self Improvement with Diverse Reasoning Chains
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
Large language models (LLMs) have achieved remarkable performance in recent years but are fundamentally limited by the underlying training data. To improve models beyond the training data, recent works have explored how LLMs can be used to generate synthetic data for autonomous self-improvement. However, successive steps of self-improvement can reach a point of diminishing returns. In this work, we propose a complementary approach towards self-improvement where finetuning is applied to a multiagent society of language models. A group of language models, all starting from the same base model, are independently specialized by updating each one using data generated through multiagent interactions among the models. By training each model on independent sets of data, we illustrate how this approach enables specialization across models and diversification over the set of models. As a result, our overall system is able to preserve diverse reasoning chains and autonomously improve over many more rounds of fine-tuning than single-agent self-improvement methods. We quantitatively illustrate the efficacy of the approach across a wide suite of reasoning tasks.
Forward citations
Cited by 5 Pith papers
-
Chasing Moving Targets with Online Self-Play Reinforcement Learning for Safer Language Models
Online self-play between attacker and defender roles of a single LLM improves safety robustness and attack diversity across Llama and Qwen models.
-
Learning Decentralized LLM Collaboration with Multi-Agent Actor Critic
Multi-agent actor-critic methods with a centralized critic improve decentralized LLM collaboration over Monte Carlo baselines in long-horizon and sparse-reward settings.
-
OSC: Cognitive Orchestration through Dynamic Knowledge Alignment in Multi-Agent LLM Collaboration
OSC uses learned Collaborator Knowledge Models and RL-trained communication policies to make LLM agents communicate adaptively, claiming gains on AlpacaEval 2.0 and MT-Bench.
-
SWIRL: A Staged Workflow for Interleaved Reinforcement Learning in Mobile GUI Control
A multi-agent RL workflow that interleaves single-agent updates, applied to mobile GUI control, achieves SOTA zero-shot performance and a +14.8 MATH500 gain.
-
Grounding Natural Language for Multi-agent Decision-Making with Multi-agentic LLMs
A design framework for multi-agent LLMs combining prompting, memory, multimodal input, and fine-tuning, with promised ablations on social-dilemma games that are absent from the supplied text.
Discussion (0). Sign in to comment.