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The paper investigates whether large language models (LLMs) can learn from rewards presented in context, a process termed in‑context reinforcement learning (ICRL). Through experiments on four benchmarks and six models, the authors find that while LLMs read reward signals, altering or removing these signals has little effect on performance, and that trajectory structure—rather than reward content—drives improvement. These results suggest that direct ICRL behaves more like standard in‑context learning (ICL) than true inference‑time reinforcement learning.
The paper investigates whether large language models (LLMs) can learn from rewards presented in context, a process termed in-context reinforcement learning (ICRL). Experiments across four benchmarks and six models show that while LLMs read reward signals, altering or removing these rewards has little impact on performance, and shuffled or corrupted trajectories are as effective as real ones. These findings suggest that direct ICRL behaves more like in-context learning (ICL) than true inference-time reinforcement learning.
Effective machine learning depends not only on how we model data, but also on what data we choose to collect. While large sequence models have revolutionized data modeling, the problem of automated data selection, or "intrinsic curiosity", remains a significant challenge.
arXiv:2606. 19476v1 Announce Type: cross Abstract: Effective machine learning depends not only on how we model data, but also on what data we choose to collect.
The paper introduces Agentic In-Context Learning (Agentic ICL), a framework where fixed‑parameter Transformers learn from interactions that shape the context they receive. It models an interactive learner with state, update, and decision rules, covering active learning, bandits, and online reinforcement learning, and demonstrates that causal Transformers can approximate these algorithms over a finite horizon. Experiments show that Transformers can learn to execute recursions and that different reward signals produce distinct decision policies, with alignment between process supervision and outcome‑optimal actions influencing learned behavior.
arXiv:2607. 11906v1 Announce Type: new Abstract: The development of decision-pretrained transformers, algorithm distillation, long-context meta-RL, and retrieval-augmented agents has renewed interest in in-context reinforcement learning (ICRL): the ability of a pretrained or fine-tuned decision model to infer latent task rules and improve future behavior from interaction context, without test-time parameter updates.