arXiv AI
HiDiffTIR: Hierarchical Difficulty-Aware Policy Optimization for Multi-Turn Tool-Integrated Reasoning
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arXiv AI
Jun 16
PACT: Privileged Trace Co-Training for Multi-Turn Tool-Use Agents
arXiv:2606. 16215v1 Announce Type: cross Abstract: Multi-turn tool-use agents must reason, call tools, and adapt to observations across several interaction turns.
arXiv AI
Jun 30
SCRIBE: Structured Mid-Level Supervision for Tool-Using Language Models
arXiv:2601. 03555v3 Announce Type: replace Abstract: Training reliable tool-augmented agents remains a significant challenge, largely due to the difficulty of credit assignment in multi-step reasoning.
arXiv AI
Jul 23
In-the-Flow Agentic System Optimization for Effective Planning and Tool Use
arXiv:2510. 05592v2 Announce Type: replace Abstract: Outcome-driven reinforcement learning has advanced reasoning in large language models (LLMs), but prevailing tool-augmented approaches train a single, monolithic policy that interleaves thoughts and tool calls under full context; this scales poorly with long horizons and diverse tools and generalizes weakly to new scenarios.
arXiv AI
Jun 2
Learning When Not to Act: Mitigating Tool Abuse in Agentic Reinforcement Learning
arXiv:2606. 02132v1 Announce Type: new Abstract: Agentic reinforcement learning can induce tool abuse, where models overuse external tools even for queries solvable by internal reasoning.
arXiv AI
Jun 3
Tool-Aware Optimization with Entropy Guidance for Efficient Agentic Reinforcement Learning
arXiv:2606. 03762v1 Announce Type: cross Abstract: Agentic reinforcement learning (RL) equips large language models (LLMs) with tool-use capabilities that substantially improve reasoning on complex tasks.
arXiv AI
Oct 2
Reducing Cognitive Overhead in Tool Use via Multi-Small-Agent Reinforcement Learning
The paper introduces MSARL, a Multi‑Small‑Agent Reinforcement Learning framework that separates reasoning from tool use. A single Reasoning Agent plans and decomposes tasks, while multiple specialized Tool Agents handle specific external tools, each trained with imitation learning and reinforcement learning using role‑specific rewards. Experiments on mathematical problem solving with code execution show that MSARL improves reasoning stability and final‑answer accuracy compared to single‑agent baselines, and the architecture generalizes to other tool‑use tasks.