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新论文 · v1arXiv:2611.11848

Causal Signal Credit Assignment for Verilog Code Generation via Counterfactual Hardware Verification

Yali Du, Ming Li

原文摘要

Large language models have improved Verilog generation, yet functional correctness remains difficult to optimize because simulation commonly provides a coarse module-level pass/fail outcome. Such feedback discards useful evidence from partially correct RTL modules and cannot determine whether a source statement is functionally responsible for an observed output behavior. We present a new method named Causal Signal Credit Assignment (CSCA), which is a counterfactual preference-optimization framework for Verilog generation. CSCA first performs continuous signal-level behavioral verification on sampled candidate modules. It then locates output-driving assignment spans and applies five interventions while holding the remaining module and testbench fixed. The resulting changes in signal-level errors are combined with behavioral agreement to form a continuous statement credit, which is mapped to response-token weights for strict weighted direct preference optimization. This enables CSCA to learn from locally supported statements without uniformly reinforcing an otherwise incorrect response. Built on Icarus Verilog and a CodeV-derived corpus, CSCA is evaluated on VerilogEval~2.0 and RTLLM~v2.0, including both code completion and specification-to-RTL tasks. With a Qwen2.5-Coder-7B-Instruct backbone, CSCA leads the evaluated 7B-scale models and remains comparable to the much larger GPT-5.4-mini on the reported Verilog-generation benchmarks.

cs.AI · cs.SE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.08335

Discovering Update Rules for Opponent Shaping in General-Sum Games

Hong Jun Lee, Ziyan Wang, Jun Wang, Stefanos Leonardos, Yali Du

原文摘要

In general-sum games, self-interested learners can reach outcomes that are poor for everyone, which has motivated methods that shape the opponent's learning. Methods like Learning with Opponent-Learning Awareness (LOLA), which differentiates through the opponent's learning process, and Model-Free Opponent Shaping (M-FOS), which uses model-free optimisation methods for long-horizon opponent shaping, have enabled mutual cooperation and exploitation of learners. However, these methods require access to the opponent's parameters, policy, rewards or learning rule during deployment. In single-agent reinforcement learning, update rules discovered by meta-learning have achieved state-of-the-art results (e.g. DiscoRL). These rules are separate from the policy they train and read only the agent's own experience. We hereby extend this framework to general-sum settings by introducing Discovered Update Rule for Opponent Shaping (DUOS). To the best of our knowledge, this is the first method to discover an update rule for an agent in general-sum games. DUOS meta-trains the rule to maximise the agent's expected return over its lifetime, and updates an inner network using the agent's own experience. We show that our discovered rule exhibits behaviour consistent with opponent shaping without reading opponent policies or parameters, and ranks 1st in normalised mean score and 1st in self-play among tournament entrants.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06976

On the Opportunities and Challenges of Prefill-to-Autoregressive Cross-Model Transfer

Zhenyi Shen, Junru Lu, Wei Liu, Jiangnan Ye, Jiazheng Li, Di Yin, Xing Sun, Tameem Adel 等 11 位作者

原文摘要

Prefill-to-Autoregressive Cross-model Transfer (PACT), a form of latent communication between language models, combines a larger model's prompt representations with a smaller model's efficient generation, offering a promising way to balance capability and inference cost. However, existing systems provide limited support for independently evaluating large-small model collaboration, and improved task performance alone does not establish whether the receiver benefits from the sender's information. We introduce PLatour, an open-source PACT framework that connects frozen models through lightweight, zero-initialized linear bridges trained directly with task rewards and supports hybrid-attention architectures. PLatour significantly outperforms open-source baselines on generation tasks and surpasses Mostik's reported results on shared mathematics and coding benchmarks using a substantially smaller sender. However, controlled experiments show that the gains on some tasks can be reproduced by uninformative sender signals, self-communication, or receiver-only training. We find that trainable bridges can act as parameter-efficient adaptation mechanisms without exploiting useful sender information. We therefore propose PACT-Check to quantify how much of a trained PACT system's gain genuinely comes from communication. Finally, we discuss the opportunities and challenges of PACT systems. Code is available at https://github.com/zhenyi4/platour.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03227

What Structurally Breaks an Agent? Auditing Difficulty in Stateful Tool-Use Workflows

Shereen Anand, Ju Wang, Yali Du

原文摘要

An aggregate task score does not distinguish failures of scheduling, state retention, and candidate selection. We develop a small executable instrument that varies dependency depth, candidate count, active state width, and access to current-state snapshots while holding the number of required operations fixed. Two dependency-graph families show that increasing depth at fixed work changes both scheduling freedom and the number of reachable completion states, so these controls are coupled, and we quantify the coupling. The primary study contains 49,152 generated tasks and 393,216 reference-controller rollouts. A separately frozen follow-on design adds 295,936 rollouts covering retention strategy, snapshot cadence, hidden state resampling, recovery, query locality, candidate indexing, and relabeling. Residue caches and precomputed plans solve tasks that defeat limited raw-value caches, and indexed selection removes the serial-scan branching deficit. Hidden resampling imposes an exact success ceiling that additional state tracking cannot overcome. The contribution is an executable diagnostic instrument, analytic calibration, and an audit of its assumptions. A small local CPU study with a 0.5B model completed no workflows, and a 16-episode Copilot-routed fractional factorial is descriptive only. Neither supports general capability claims.

cs.LG

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arXiv ↗PDF ↗