Auto-bidding is a long-horizon sequential decision problem for maximizing conversion value under budget and key performance indicator (KPI) constraints. Recent work extends this task from bidding alone to joint bidding and pricing, where a policy controls bidding decisions and pricing corrections. Existing methods mainly rely on numerical trajectory modeling, which offers limited support for interpreting campaign context and expressing high-level strategies. Large language models (LLMs) can complement this paradigm with their reasoning capabilities. However, existing language-guided methods have two limitations. First, they condition actions on language strategies without modeling the corresponding state changes, making it difficult to distinguish errors in strategy understanding from errors in action generation. Second, different instructions can produce similar execution effects, leading to imbalanced policy updates across effects. We propose LangBP, a hierarchical framework for language-guided joint bidding and pricing. LangBP's Semantic Decision Transformer (S-DT) predicts target states from the instruction and the trajectory history, then recovers the joint action via inverse dynamics. We further propose Execution-Grouped Policy Optimization (EGPO), which scores candidate effects with a Context--Effect Verifier (CEV) and balances policy updates across effect groups. Experiments on AuctionNet show that LangBP outperforms strong baselines, and online A/B tests further demonstrate business gains in real-world deployment on a large-scale e-commerce platform.

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