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This paper introduces the concept of memory depth for long-running language agents, distinguishing it from retrieval-based memory access, and proposes EVAF, a selective parametric consolidation mechanism using surprise- and valence-gated LoRA updates. Experiments across multiple models show EVAF improves goal persistence after context unload with minimal parametric writes.
OPID proposes an on-policy skill distillation framework that extracts dense hindsight supervision from completed trajectories, combining outcome-based RL with token-level self-distillation to improve language agent training efficiency and performance on multi-turn tasks.
This paper formulates memory retention for long-horizon language agents as a constrained stochastic optimization problem, introducing OSL-MR, a framework that enforces observability-safe learning with a Mixed-Score heuristic. Experiments show consistent improvements over existing heuristic baselines under tight memory budgets.
This paper investigates whether spatial geometry improves language-agent memory recall, demonstrating that geometry must lead recall over recency and importance, and that a ray-tracing visibility predicate is crucial for occlusion handling in 3D voxel worlds.
Proposes CVT-RL, a constrained policy-gradient algorithm with policy-conditioned counterfactual contribution estimation and verifiable rewards, improving long-horizon language agent reliability and reducing reward hacking.
This paper introduces ArcANE, an automatically constructed benchmark for evaluating role-playing language agents' alignment with character psychological trajectories across narrative phases, showing that conditioning on character arc information improves performance, especially in scenarios beyond the source text.
A comprehensive evaluation framework for continual learning in language agents is introduced, emphasizing controlled task streams and memory design analysis to better assess reusable experience and learning stability.
This paper introduces PaW, a co-training framework that adds auxiliary world modeling supervision to policy learning during on-policy RL rollouts, improving language agent training without additional computational overhead.
LiteCoder-Terminal-Gen introduces a zero-dependency synthetic pipeline that generates executable terminal training environments, producing SFT and RL datasets that enable language agents to achieve significant performance gains on Terminal Bench benchmarks.
RICE-PO is a critic-free policy optimization framework that turns retrieval interactions into localized credit signals for training reasoning agents, outperforming prompt-based and group-based RL baselines on BRIGHT and BEIR benchmarks.
This paper systematically evaluates model-generated skills for language agents across the full lifecycle of experience generation, extraction, and consumption, finding that skills are beneficial on average but exhibit non-trivial negative transfer, leading to a meta-skill that improves skill quality.
Auto-Dreamer introduces a learned offline memory consolidation method for language agents, decoupling fast memory acquisition from slow cross-session consolidation, and achieving higher performance with smaller memory banks, generalizing to unseen environments.
OpenAgents is an open platform for using and hosting language agents in everyday life, featuring agents for data analysis, plugins, and web browsing, with open code and a demo.
rLLM is an open-source framework for post-training language agents via reinforcement learning, with notable model releases like DeepSWE-Preview and DeepCoder-14B-Preview achieving state-of-the-art results.
This paper introduces BEACON, a milestone-guided policy learning framework designed to improve credit assignment and sample efficiency for long-horizon language agents. It demonstrates significant performance improvements over GRPO and GiGPO on benchmarks like ALFWorld, WebShop, and ScienceWorld.