Papers with post-training framework
FAITH: Factuality Alignment through Integrating Trustworthiness and Honestness (2026.findings-acl)
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| Challenge: | Existing approaches to correct factually inaccurate outputs are lacking the semantic richness needed to properly understand its internal states of trustworthiness and honesty. |
| Approach: | They propose a framework for factuality alignment that integrates natural-language uncertainty signals with external knowledge and computes confidence scores and semantic entropy from LLM outputs. |
| Outcome: | Extensive experiments on four knowledge-intensive benchmarks show that FAITH improves the factual accuracy and truthfulness of Large Language Models (LLMs). |
Lightweight Haar Wavelet Subband Pruning for LLMs (2026.findings-acl)
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| Challenge: | Large language models (LLMs) have impressive performance but require computational and memory resources. |
| Approach: | They propose a post-training framework that uses a Haar wavelet transform to prune weights. |
| Outcome: | The proposed pruning framework reduces pruning time and computational costs by removing less important weights while preserving model architecture. |
TRN-R1-Zero: Text-rich Network Reasoning via LLMs with Reinforcement Learning Only (2026.acl-long)
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| Challenge: | Recent large language model-based approaches often overlook graph context or depend on distillation from larger models, limiting generalisation. |
| Approach: | They propose a framework for zero-shot reasoning on text-rich networks . they use a Neighbour-aware Group Relative Policy Optimisation objective . |
| Outcome: | The proposed framework optimises base LLMs using a Neighbour-aware group relative policy optimisation objective based on a novel margin gain metric for the informativeness of neighbouring signals . |
Mitigating Hallucinations in LM-Based TTS Models via Distribution Alignment Using GFlowNets (2025.emnlp-main)
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| Challenge: | Existing mitigation strategies for Text-to-Speech systems require excessive training resources or inference latency. |
| Approach: | They propose a GFlOwNet-guided distribution AlignmenT framework that mitigates hallucinations without relying on massive resources or inference latency. |
| Outcome: | The proposed framework reduces over 50% character error rates and lowers uncertainty by up to 58% on challenging test cases. |
Step-GRPO: Internalizing Dynamic Early Exit for Efficient Reasoning (2026.acl-long)
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| Challenge: | Large reasoning models that use long chain-of-thought excel at problem-solving but waste computational resources. |
| Approach: | They propose a framework that internalizes dynamic early-exit capabilities directly into the model. |
| Outcome: | The proposed framework reduces token consumption by 32.0% on a Qwen3-8B model compared to the vanilla model . |
StateX: Enhancing RNN Recall via Post-training State Expansion (2026.findings-acl)
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| Challenge: | Existing studies show that RNNs with large recurrent states are expensive to train . however, the ability to recall contextual information from long contexts is underperforms them in certain aspects. |
| Approach: | They propose a framework that expands the states of pre-trained RNNs by scaling them up to 1.3B . they use a recurrent architecture that compresses contextual information into a fixedsize state . |
| Outcome: | Experiments on models with up to 1.3B parameters show that StateX expands state sizes without incurring high post-training costs or compromising other capabilities. |