arXiv AI
RTL-Interact: Training EDA-Interactive Models for Multi-Task RTL Design
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arXiv Machine Learning
Sep 24
ChipMEM: Verification-Grounded Memory for EDA Agents
ChipMEM introduces a verification‑grounded memory layer for electronic design automation agents that combines cross‑task procedural memory with within‑trajectory statistical guidance. The procedural component stores a skill only after it passes synthesis, simulation, or formal checks, while a Bayesian component ranks recovery strategies based on tool‑call outcomes. Experiments on RTLRewriter‑Bench and CVDP tasks show that ChipMEM improves equivalence‑passing outputs and area metrics compared to agents without memory.
arXiv Computation and Language
Sep 18
CovR: Coverage-Aware Hardware Verification via Reasoning-Guided Reinforcement Learning
CovR is an agentic framework that automates testbench generation for hardware verification by combining self-reflection loops with simulation-based feedback to maximize coverage. It builds a large dataset of 16,514 specification–RTL reasoning tuples and uses reinforcement learning with tool-derived rewards to train a student model, achieving high coverage scores on VerilogEval, RTLLM V2.0, and CVDP. When deployed as a plug-in stimulus engine, CovR boosts coverage by nearly 19% and improves mutation detection while uncovering previously undetected failures.
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
Jun 3
Synthesize and Reward -- Reinforcement Learning for Multi-Step Tool Use in Live Environments
arXiv:2606. 03892v1 Announce Type: cross Abstract: Training LLMs to orchestrate multi-step tool calls is held back by three coupled obstacles: realistic stateful execution environments are costly to build, synthetic training queries are often detached from the server's actual state (so the generated tool calls fail to execute), and recall-based RL rewards incentivize verbose tool-calling patterns.
arXiv Computation and Language
Oct 16
Composition-RL: Compose Your Verifiable Prompts for Reinforcement Learning of Large Language Models
Composition‑RL is an offline data‑augmentation method that composes existing prompts before reinforcement learning to address the pass‑rate‑1 bottleneck in large‑language‑model training. By generating and verifying a composed dataset once from a fixed source pool, it avoids modifying the RL optimizer and improves reasoning performance across models ranging from 4B to 30B. A curriculum variant that gradually increases compositional depth further boosts performance, and the approach also facilitates cross‑domain RL by combining prompts from different domains.
arXiv AI
Sep 15
Not All Prompts Are Equal: Exploration-Guided Prompt Scaffolding for Multimodal Reinforcement Post-Training
The paper introduces an exploration-guided prompt scaffolding framework for multimodal large language models, dynamically adjusting the prompt distribution during reinforcement learning post-training. It uses an Exploration Potential Score (EPS) derived from KL-regularized policy improvement to assess prompt utility without extra overhead, and a teacher model rewrites low-utility prompts to preserve intent while improving informativeness. Experiments on Geo3K, MMK12, MathVision, and MMMU-Pro show consistent performance gains, up to 9.7% in-domain and over 11% on out-of-distribution benchmarks.