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// 75d agoRESEARCH PAPER
ICRL paper pushes RL-only tool-use training
In-Context Reinforcement Learning (ICRL) proposes an RL-only framework for LLM tool use that avoids supervised fine-tuning by injecting few-shot examples into rollout prompts, then tapering to zero-shot tool calling. The paper reports state-of-the-art results across reasoning and tool-use benchmarks, arguing for a more scalable and data-efficient training path.
// ANALYSIS
This is a meaningful challenge to the standard SFT-then-RL recipe, especially for teams bottlenecked on labeled tool-use traces.
- –The curriculum-style shift from few-shot rollouts to zero-shot behavior is a practical way to teach tool invocation without permanent prompt crutches.
- –If the gains hold across broader agent settings, RL-only pipelines could materially cut annotation cost and iteration time.
- –The strongest impact is likely on tool-heavy agent stacks where reliability and sample efficiency matter more than raw model scale.
// TAGS
icrlllmagentreasoningresearchbenchmark
DISCOVERED
75d ago
2026-03-14
PUBLISHED
75d ago
2026-03-14
RELEVANCE
9/ 10
AUTHOR
Discover AI