A prevalent approach to interpretable representation learning involves creating a mask that weights the significance of each input feature, followed by deriving a masked representation by applying this mask to the input representation. However, the identifiability of these learned masked representations is often uncertain, making the origin of these representations ambiguous or unreliable. Furthermore, the approaches to interpreting Transformer based on attention weights have been criticized for their faithfulness. To address these limitations, we propose a novel causal framework that directly learns identifiable and explainable representations from attention weights, rather than relying on importance masks. Our framework leverages identifiability theory and causal representation learning to extract explainable representations within a subspace of input representations, effectively transforming frozen representation learning methods into self-explaining systems. Experimental results on real-world datasets demonstrate that, compared to well-established state-of-the-art methods, our approach provides identifiable and more trustworthy explanations while guaranteeing faithfulness.

Group-Relative Policy Optimization (GRPO) is widely used to post-train LLM agents, but its trajectory-level advantage is ill-suited to long-horizon credit assignment. Reward-homogeneous rollout groups yield zero advantage; otherwise, one scalar uniformly scales every token, obscuring which decisions determine the outcome. We propose **TRACE-GRPO** (**T**eacher-**R**efined **A**dvantage **C**redit **E**stimation for GRPO), which converts teacher-LLM feedback into hierarchical credit. Teacher scores provide a sample-level fallback for zero-variance groups, reweight trajectory advantage at the turn level, and redistribute turn credit at the token level using the policy’s log-probability shift under a teacher critique. This yields dense training-time credit without token-level annotations or learned critics. We also introduce **CAPA** (Critique-Aware Paged Attention), which reuses trajectory KV caches and reduces the isolated critique-conditioned forward from roughly 2× a vanilla GRPO step to approximately 5% of a training step. On ALFWorld and ScienceWorld with Qwen2.5-7B-Instruct, TRACE-GRPO achieves 96.3% and 73.4% success, respectively, outperforming strong agent RL baselines. Ablations show cumulative gains from the three credit levels, and comparison with supervised fine-tuning on teacher-generated successes confirms that critique-guided policy optimization provides benefits beyond imitation. Code is available at [https://github.com/Lau-Jonathan/TRACE-GRPO](https://github.com/Lau-Jonathan/TRACE-GRPO).