NBPO (finite-pool realisation) on TL;DR
Nash Bargaining Preference Optimization, the finite-pool realisation of Algorithm 1 at the documented eta = 1.0: projected dual descent on the frozen response pool, sampled pair targets, sequence-sum regression.
- Panel: TL;DR
- Objectives: coverage, faithfulness, conciseness, helpfulness
- Backbone / reference policy:
meta-llama/Llama-3.1-8B-Instruct - Training budget: 300 optimiser updates, global batch 16
- Reported in: Nash Bargaining Preference Optimization (NBPO), Table 2 (primary cross-method evaluation)
Evaluation protocol: independent objective-wise win rate against the common reference,
judged by Llama-3.3-70B-Instruct on prompt-disjoint held-out prompts, both presentation orders.