Exploit More, Explore Smarter for Budget-Constrained Agentic Search
cs.AI, cs.LG
Submitted: 2026-08-24
Updated: 2026-08-24
Terminology
Sources
- GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning
- SWE-Search: Enhancing Software Agents with Monte Carlo Tree Search and Iterative Refinement
- Large Language Monkeys: Scaling Inference Compute with Repeated Sampling
- LLMZero: Discovering Adaptive Training Strategies for RL Post-Training via LLM Agents
- Optimizing CUDA like a Human: Micro-Profiling Tools as Expert Surrogates for LLM-Based GPU Kernel Optimization
- Reasoning with Language Model is Planning with World Model
- ReSkill: Reconciling Skill Creation with Policy Optimization in Agentic RL
- LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code
- DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines
- Efficient Memory Management for Large Language Model Serving with PagedAttention
- S*: Test Time Scaling for Code Generation
- Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
- Spend Less, Reason Better: Budget-Aware Value Tree Search for LLM Agents
- EvoX: Meta-Evolution for Automated Discovery
- ML-Master: Towards AI-for-AI via Integration of Exploration and Reasoning
- Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers
- Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
- Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
- Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
- Qwen3 Technical Report
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