Papers with TRAC
Exploring the Capacity of Pretrained Language Models for Reasoning about Actions and Change (2023.acl-long)
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| Challenge: | Recent transformer-based language models (LMs) provide reasoning over textual benchmarks . RAC is essential to understand and interact with the ever-changing environment . |
| Approach: | They propose to use a transformer-based language model to learn to reason over textual benchmarks. |
| Outcome: | The proposed model minimizes the influence of other linguistic requirements to focus on RAC. |
TRAC: Token-level Reward Assignment for Coherent Abstractive Summarization (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have achieved remarkable success in text summarization, but maintaining logical coherence and contextual consistency remains a pervasive challenge in long-form generation. |
| Approach: | They propose a framework that introduces a token-level reward function by integrating relative sentence gain, inter-sentence attention, and a Gaussian length penalty. |
| Outcome: | The proposed model outperforms the sequence-level baseline by 11.05% in fluency and 10.61% in Relevance. |
TRAC: Teacher-Guided Token Reward with Adaptive Calibration for Robust Policy Optimization (2026.acl-long)
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| Challenge: | Current reward models for reinforcement learning (RL) rely on outcome rewards that propagate a single scalar value across all tokens based on final correctness. |
| Approach: | They propose a framework that derives dense token-level supervision from LLMs . they use a multi-granularity calibration mechanism to modulate teacher influence . |
| Outcome: | The proposed framework evaluates teacher reliability across problem-level expertise, trajectory-level discrimination, and token-level confidence. |