MaxIM frames incremental memory summarization as an RL reward-shaping problem
Original title: MaxIM: Maximally Informative Incremental Summarization via Reinforcement Learning
A variational mutual-information critic supplies dense rewards so bounded summaries stay predictive of future task targets.
Researchers introduce MaxIM, a framework that treats incremental summarization as a capacity-constrained, information-theoretic reward-shaping problem. It maximizes a variational lower bound on predictive sufficiency and uses a recursive consistency condition so summaries stay grounded in the observed history. A parameterized asymmetric critic computes step-wise variational mutual-information bounds to ease sparse-reward credit assignment. On long-horizon scientific reading and multi-turn dialogue benchmarks, the authors report better factual grounding and downstream utility than RL, supervised fine-tuning, and prompt baselines, without human preference labels.
- Sparse delayed rewards make standard RL hard for memory updates.
- The critic shapes dense rewards from variational mutual-information bounds.
- Evaluation covers scientific reading and multi-turn dialogue under token budgets.
- Authors say no expensive human preference labels are required.
From the original article
1 min read at arxiv.orgAI systems must often maintain bounded memory of unbounded interaction histories. However, optimizing the memory representation, and its updates, for downstream task utility is difficult for standard reinforcement learning (RL) due to severe credit assignment bottlenecks induced by sparse, delayed rewards.
By Jihwan Jeong · arxiv.org
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