arXiv AI
Rewards for Budget-Aware Multi-Turn Agents That Adapt to Arbitrary Test-Time Constraints
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arXiv AI
Sep 2
One Policy, Any Budget: Internalizing Budget-Aware Search via Reinforcement Learning
The paper introduces AnySearch, a reinforcement‑learning framework that trains a single policy to perform budget‑aware search for large language models under any budget constraint. The training proceeds in two phases: first, the agent learns with explicit budget state injection and structured reasoning prompts under linearly decaying budgets; second, the scaffold is removed and the agent adapts to randomly sampled budgets that match deployment conditions. The reward combines answer accuracy and budget efficiency, with adaptive weighting to emphasize efficiency for high‑accuracy queries and reduce it for low‑accuracy ones. Experiments on seven QA benchmarks demonstrate that AnySearch outperforms baselines across all budget scales, generalizes to unseen constraints, and improves tool productivity without excessive token overhead.
arXiv AI
Oct 9
On the Clock: Towards Punctual and Productive Time-Budgeted AI Agents
The paper investigates whether small large language model agents can operate within explicit wall‑clock time budgets while using the allotted time productively. Experiments with Qwen3.6‑27B on MLE‑Bench Lite and Qwen3‑4B on Zork I show that agents fail to translate budget statements into controlled time use, largely due to lack of timing feedback and poor anticipation of action durations. Interventions such as harness‑based timing exposure, enforcement hooks, and reinforcement learning with budget‑aware rewards improve budget adherence but do not lead to better task performance, revealing a persistent gap between respecting time limits and effectively allocating extra time.
arXiv AI
Jun 10
TRACE: A Unified Rollout Budget Allocation Framework for Efficient Agentic Reinforcement Learning
arXiv:2606. 11119v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) is a promising approach for enhancing reasoning and agentic behavior in large language models.
arXiv Machine Learning
Jul 16
Where Should RL Post-Training Compute Go? Model Size, Search, Learning, and Feedback
arXiv:2607. 13389v1 Announce Type: new Abstract: Reinforcement Learning (RL) post-training is increasingly used to adapt foundation models for reasoning, planning, and feedback-driven robot-learning pipelines, but constrained post-training resources are often summarized by a single total FLOP budget.
arXiv AI
Aug 7
EcoAgent-Bench: Evaluating Economic Decision-Making in Budget-Constrained LLM Agents
arXiv:2608. 05519v1 Announce Type: new Abstract: Agent benchmarks usually measure task completion and treat resource use as an auxiliary statistic.
arXiv AI
Jul 8
TurnOPD: Making On-Policy Distillation Turn-Aware for Efficient Long-Horizon Agent Training
arXiv:2607. 05804v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student policy by matching a stronger teacher on the student's own trajectories, offering a promising framework for language agent training.